README.md

Summary: #Data Visualization with D3.js Final Project

Author : Nok Chan

Date: 2016/08/16

This is the final project Data Visualization with D3.js course, which is part of the Udacity Data Analyst Nanodegree.

Date set: Propser Loan Data Set:

Used skills: d3.js , R ,HTML, CSS

##Summary

This project used the d3.js library to build a boxplot visualization. The data is subseted using R to select the interest features and output the dataset file "loandata.csv". The data is read using script written in the index.html The boxplot is aim to demonstrate the relation between different variable to answer the question that what factor affect the loan amount and who borrow the most. In general, the loan amount is increase with income range, though the $0 group is a special one, which has a larger median than $1-24,999 group. It is interesting as it shows that sometime not the richer one will borrow more. Home Owner is another variable that added into the visualization, in all income range group, home owner borrows more than non- home owner.

Variables in this visualization: LoanOriginalAmount, IncomeRange,IsBorrowerHomeOwner

##Design

###Box plot

Why Box Plot?

I choose box plot as I think it is the best way to demonstrate the distribution and trend of the data. There is a linear trend with loan amount and income range, it can be demonstrated by the median of each box plot. However, it will be not appropiate to only demonstrate the median, as the range also changes, the box plot is the best way to exhibit both the trend and distribution in one visualization.

I have use a boxplot template and modify it and change the process of reading data as the format are not the same. I also build a element at first such that everytime you click the button, it will remove the child element and build the new graph. In this way, the structure of the html will be stable, and the words originally below the graph will not change their position. Originally I delete the whole element and build up a new graph, it will make the words under the graph move after clicked.

Goal

A box plot is build to show the distribution of loan amount in different subset of variable. Peopple can click the button to see loan amount in home owner group in different income range or non- home owner. I have considered to make both plot in one big graph, but after my consideration, I think it will be better that people can click with options and interact with the data. People should be able to discover that Home owner always borrow more than Non-homeowner, and loan amount increase with income range generally.

##Reference list:

http://bl.ocks.org/jensgrubert/7789216 https://discussions.udacity.com/t/final-project-boxplot/183468/18

##Resource: Prosper Loan Data Set

box.js

(function() {

// Inspired by http://informationandvisualization.de/blog/box-plot
d3.box = function() {
  var width = 1,
      height = 1,
      duration = 0,
      domain = null,
      value = Number,
      whiskers = boxWhiskers,
      quartiles = boxQuartiles,
      showLabels = true, // whether or not to show text labels
      numBars = 4,
      curBar = 1,
      tickFormat = null;

  // For each small multiple…
  function box(g) {
    g.each(function(data, i) {
      //d = d.map(value).sort(d3.ascending);
      //var boxIndex = data[0];
      //var boxIndex = 1;
      var d = data[1].sort(d3.ascending);

     // console.log(boxIndex);
      //console.log(d);

      var g = d3.select(this),
          n = d.length,
          min = d[0],
          max = d[n - 1];

      // Compute quartiles. Must return exactly 3 elements.
      var quartileData = d.quartiles = quartiles(d);

      // Compute whiskers. Must return exactly 2 elements, or null.
      var whiskerIndices = whiskers && whiskers.call(this, d, i),
          whiskerData = whiskerIndices && whiskerIndices.map(function(i) { return d[i]; });

      // Compute outliers. If no whiskers are specified, all data are "outliers".
      // We compute the outliers as indices, so that we can join across transitions!
      var outlierIndices = whiskerIndices
          ? d3.range(0, whiskerIndices[0]).concat(d3.range(whiskerIndices[1] + 1, n))
          : d3.range(n);

      // Compute the new x-scale.
      var x1 = d3.scale.linear()
          .domain(domain && domain.call(this, d, i) || [min, max])
          .range([height, 0]);

      // Retrieve the old x-scale, if this is an update.
      var x0 = this.__chart__ || d3.scale.linear()
          .domain([0, Infinity])
         // .domain([0, max])
          .range(x1.range());

      // Stash the new scale.
      this.__chart__ = x1;

      // Note: the box, median, and box tick elements are fixed in number,
      // so we only have to handle enter and update. In contrast, the outliers
      // and other elements are variable, so we need to exit them! Variable
      // elements also fade in and out.

      // Update center line: the vertical line spanning the whiskers.
      var center = g.selectAll("line.center")
          .data(whiskerData ? [whiskerData] : []);

     //vertical line
      center.enter().insert("line", "rect")
          .attr("class", "center")
          .attr("x1", width / 2)
          .attr("y1", function(d) { return x0(d[0]); })
          .attr("x2", width / 2)
          .attr("y2", function(d) { return x0(d[1]); })
          .style("opacity", 1e-6)
        .transition()
          .duration(duration)
          .style("opacity", 1)
          .attr("y1", function(d) { return x1(d[0]); })
          .attr("y2", function(d) { return x1(d[1]); });

      center.transition()
          .duration(duration)
          .style("opacity", 1)
          .attr("y1", function(d) { return x1(d[0]); })
          .attr("y2", function(d) { return x1(d[1]); });

      center.exit().transition()
          .duration(duration)
          .style("opacity", 1e-6)
          .attr("y1", function(d) { return x1(d[0]); })
          .attr("y2", function(d) { return x1(d[1]); })
          .remove();

      // Update innerquartile box.
      var box = g.selectAll("rect.box")
          .data([quartileData]);

      box.enter().append("rect")
          .attr("class", "box")
          .attr("x", 0)
          .attr("y", function(d) { return x0(d[2]); })
          .attr("width", width)
          .attr("height", function(d) { return x0(d[0]) - x0(d[2]); })
        .transition()
          .duration(duration)
          .attr("y", function(d) { return x1(d[2]); })
          .attr("height", function(d) { return x1(d[0]) - x1(d[2]); });

      box.transition()
          .duration(duration)
          .attr("y", function(d) { return x1(d[2]); })
          .attr("height", function(d) { return x1(d[0]) - x1(d[2]); });

      // Update median line.
      var medianLine = g.selectAll("line.median")
          .data([quartileData[1]]);

      medianLine.enter().append("line")
          .attr("class", "median")
          .attr("x1", 0)
          .attr("y1", x0)
          .attr("x2", width)
          .attr("y2", x0)
        .transition()
          .duration(duration)
          .attr("y1", x1)
          .attr("y2", x1);

      medianLine.transition()
          .duration(duration)
          .attr("y1", x1)
          .attr("y2", x1);

      // Update whiskers.
      var whisker = g.selectAll("line.whisker")
          .data(whiskerData || []);

      whisker.enter().insert("line", "circle, text")
          .attr("class", "whisker")
          .attr("x1", 0)
          .attr("y1", x0)
          .attr("x2", 0 + width)
          .attr("y2", x0)
          .style("opacity", 1e-6)
        .transition()
          .duration(duration)
          .attr("y1", x1)
          .attr("y2", x1)
          .style("opacity", 1);

      whisker.transition()
          .duration(duration)
          .attr("y1", x1)
          .attr("y2", x1)
          .style("opacity", 1);

      whisker.exit().transition()
          .duration(duration)
          .attr("y1", x1)
          .attr("y2", x1)
          .style("opacity", 1e-6)
          .remove();

      // Update outliers.
      var outlier = g.selectAll("circle.outlier")
          .data(outlierIndices, Number);

      outlier.enter().insert("circle", "text")
          .attr("class", "outlier")
          .attr("r", 5)
          .attr("cx", width / 2)
          .attr("cy", function(i) { return x0(d[i]); })
          .style("opacity", 1e-6)
        .transition()
          .duration(duration)
          .attr("cy", function(i) { return x1(d[i]); })
          .style("opacity", 1);

      outlier.transition()
          .duration(duration)
          .attr("cy", function(i) { return x1(d[i]); })
          .style("opacity", 1);

      outlier.exit().transition()
          .duration(duration)
          .attr("cy", function(i) { return x1(d[i]); })
          .style("opacity", 1e-6)
          .remove();

      // Compute the tick format.
      var format = tickFormat || x1.tickFormat(8);

      // Update box ticks.
      var boxTick = g.selectAll("text.box")
          .data(quartileData);
     if(showLabels == true) {
      boxTick.enter().append("text")
          .attr("class", "box")
          .attr("dy", ".3em")
          .attr("dx", function(d, i) { return i & 1 ? 6 : -6 })
          .attr("x", function(d, i) { return i & 1 ?  + width : 0 })
          .attr("y", x0)
          .attr("text-anchor", function(d, i) { return i & 1 ? "start" : "end"; })
          .text(format)
        .transition()
          .duration(duration)
          .attr("y", x1);
    }

      boxTick.transition()
          .duration(duration)
          .text(format)
          .attr("y", x1);

      // Update whisker ticks. These are handled separately from the box
      // ticks because they may or may not exist, and we want don't want
      // to join box ticks pre-transition with whisker ticks post-.
      var whiskerTick = g.selectAll("text.whisker")
          .data(whiskerData || []);
    if(showLabels == true) {
      whiskerTick.enter().append("text")
          .attr("class", "whisker")
          .attr("dy", ".3em")
          .attr("dx", 6)
          .attr("x", width)
          .attr("y", x0)
          .text(format)
          .style("opacity", 1e-6)
        .transition()
          .duration(duration)
          .attr("y", x1)
          .style("opacity", 1);
    }
      whiskerTick.transition()
          .duration(duration)
          .text(format)
          .attr("y", x1)
          .style("opacity", 1);

      whiskerTick.exit().transition()
          .duration(duration)
          .attr("y", x1)
          .style("opacity", 1e-6)
          .remove();
    });
    d3.timer.flush();
  }

  box.width = function(x) {
    if (!arguments.length) return width;
    width = x;
    return box;
  };

  box.height = function(x) {
    if (!arguments.length) return height;
    height = x;
    return box;
  };

  box.tickFormat = function(x) {
    if (!arguments.length) return tickFormat;
    tickFormat = x;
    return box;
  };

  box.duration = function(x) {
    if (!arguments.length) return duration;
    duration = x;
    return box;
  };

  box.domain = function(x) {
    if (!arguments.length) return domain;
    domain = x == null ? x : d3.functor(x);
    return box;
  };

  box.value = function(x) {
    if (!arguments.length) return value;
    value = x;
    return box;
  };

  box.whiskers = function(x) {
    if (!arguments.length) return whiskers;
    whiskers = x;
    return box;
  };

  box.showLabels = function(x) {
    if (!arguments.length) return showLabels;
    showLabels = x;
    return box;
  };

  box.quartiles = function(x) {
    if (!arguments.length) return quartiles;
    quartiles = x;
    return box;
  };

  return box;
};

function boxWhiskers(d) {
  return [0, d.length - 1];
}

function boxQuartiles(d) {
  return [
    d3.quantile(d, .25),
    d3.quantile(d, .5),
    d3.quantile(d, .75)
  ];
}

})();

d3.v3.min.js

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index.html

<!DOCTYPE html>
<meta charset="utf-8">
<style>

body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}

.box {
  font: 10px sans-serif;
}

.box line,
.box rect,
.box circle {
  fill: steelblue;
  stroke: #000;
  stroke-width: 1px;
}

.box .center {
  stroke-dasharray: 3,3;
}

.box .outlier {
  fill: none;
  stroke: #000;
}

.axis {
  font: 12px sans-serif;
}

.axis path,
.axis line {
  fill: none;
  stroke: #000;
  shape-rendering: crispEdges;
}

.x.axis path {
  fill: none;
  stroke: #000;
  shape-rendering: crispEdges;
}

</style>
<body>

<h1> Who borrow the most money?</h1>
<div style="width: 800px";
<p> Introdcution: The Prosper Loan data set is used to build this visualization. This visualization aim to demonstrate different how different factor affect the loan amount. You can discover some trend in the visualization and get some insight about which factor affect the loan amount most.</p>
</div>

<div id="chartContainer">
<button id="btn1">Home Owner</button>
<button id="btn2">Non-Home Owner</button>
</div>
<script src="http://d3js.org/d3.v3.min.js"></script>
<script src="d3.v3.min.js"></script>
<script src="box.js"></script>
<script>

d3.select("#btn1").on("click", function() {

d3.select("svg").selectAll("*").remove(); // clean out the graph if button is click,
draw_btn1();  //replot a new one.
                    });
d3.select("#btn2").on("click", function() {
d3.select("svg").selectAll("*").remove();
draw_btn2();
                    });



var labels = true; // show the text labels beside individual boxplots?

var margin = {top: 30, right: 50, bottom: 70, left: 50};
var  width = 900 - margin.left - margin.right;
var height = 500 - margin.top - margin.bottom;

var min = Infinity,
    max = -Infinity;

// parse in the data
    var svg = d3.select("body").append("svg")
        .attr("width", width + margin.left + margin.right)
        .attr("height", height + margin.top + margin.bottom)
        .attr("class", "box")
        .append("g")
        .attr("transform", "translate(" + margin.left + "," + margin.top + ")");

draw_btn1(); /// draw this graph first

function draw_btn1(){d3.csv("loandata.csv", function(error, csv) {
    // using an array of arrays with
    // data[n][2]
    // where n = number of columns in the csv file
    // data[i][0] = name of the ith column
    // data[i][1] = array of values of ith column

    var data = [];
    data[0] = [];
    data[1] = [];
    data[2] = [];
    data[3] = [];
    data[4] = [];
    data[5] = [];

    // add more rows if your csv file has more columns

    // add here the header of the csv file
    data[0][0] = "$0";
    data[1][0] = "$1-24,999";
    data[2][0] = "$25,000-49,999";
    data[3][0] = "$50,000-74,999";
    data[4][0] = "75,000-99,999";
    data[5][0] = "100,000+";


    // add more rows if your csv file has more columns

    data[0][1] = [];
    data[1][1] = [];
    data[2][1] = [];
    data[3][1] = [];
    data[4][1] = [];
    data[5][1] = [];
    // add header
    data[0][0] = "$0";
    data[1][0] = "$1-24,999";
    data[2][0] = "$25,000-49,999";
    data[3][0] = "$50,000-74,999";
    data[4][0] = "$75,000-99,999";
    data[5][0] = "$100,000+";
    csv.forEach(function(x) {
      if (x.IsBorrowerHomeOwner =="FALSE"){
 if (x.IncomeRange == "$0") {
                        var v1 = Math.floor(x.LoanOriginalAmount);
                    }  if (x.IncomeRange == "$1-24,999") {
                        var v2 = Math.floor(x.LoanOriginalAmount);
                    } else if (x.IncomeRange == "$25,000-49,999") {
                        var v3 = Math.floor(x.LoanOriginalAmount);
                    } else if (x.IncomeRange == "$50,000-74,999") {
                        var v4 = Math.floor(x.LoanOriginalAmount);
                    } else if (x.IncomeRange == "$75,000-99,999") {
                        var v5 = Math.floor(x.LoanOriginalAmount);}
                        else if (x.IncomeRange == "$100,000+") {
                        var v6 = Math.floor(x.LoanOriginalAmount);}}
            // add more variables if your csv file has more columns

if (v1) {
                        data[0][1].push(v1);
                        if (v1 > max)
                            max = v1;
                        if (v1 < min)
                            min = v1;
                    }
                    if (v2) {
                        data[1][1].push(v2);
                        if (v2 > max)
                            max = v2;
                        if (v2 < min)
                            min = v2;
                    }
                    if (v3) {
                        data[2][1].push(v3);
                        if (v3 > max)
                            max = v3;
                        if (v3 < min)
                            min = v3;
                    }
                    if (v4) {
                        data[3][1].push(v4);
                        if (v4 > max)
                            max = v4;
                        if (v4 < min)
                            min = v4;
                    }
                    if (v5) {
                        data[4][1].push(v5);
                        if (v5 > max)
                            max = v5;
                        if (v5 < min)
                            min = v5;};
     if (v6) {
                        data[5][1].push(v6);
                        if (v6 > max)
                            max = v6;
                        if (v6 < min)
                            min = v6;}});


         // add more rows if your csv file has more columns




    var chart = d3.box()
        .whiskers(iqr(1.5))
        .height(height)
        .domain([min, max])
        .showLabels(labels);

    var svg = d3.select("body").select("svg")
        .attr("width", width + margin.left + margin.right)
        .attr("height", height + margin.top + margin.bottom)
        .attr("class", "box")
        .append("g")
        .attr("transform", "translate(" + margin.left + "," + margin.top + ")");








    // the x-axis
    var x = d3.scale.ordinal()
        .domain( data.map(function(d) { console.log(d); return d[0] } ) )
        .rangeRoundBands([0 , width], 0.7, 0.3);

    var xAxis = d3.svg.axis()
        .scale(x)
        .orient("bottom");

    // the y-axis
    var y = d3.scale.linear()
        .domain([min, max])
        .range([height + margin.top, 0 + margin.top]);

    var yAxis = d3.svg.axis()
    .scale(y)
    .orient("left");

    // draw the boxplots
    svg.selectAll(".box")
      .data(data)
      .enter().append("g")
        .attr("transform", function(d) { return "translate(" +  x(d[0])  + "," + margin.top + ")"; } )
      .call(chart.width(x.rangeBand()));



    // add a title
    svg.append("text")
        .attr("x", (width / 2))
        .attr("y", 0 )
        .attr("text-anchor", "middle")
        .style("font-size", "18px")
        //.style("text-decoration", "underline")
        .text("Loan Original Amount in different Income Range (Non-Home Owner)");

     // draw y axis
    svg.append("g")
        .attr("class", "y axis")
        .call(yAxis)
        .append("text") // and text1
          .attr("transform", "rotate(-90)")
          .attr("y", 6)
          .attr("dy", ".71em")
          .style("text-anchor", "end")
          .style("font-size", "16px")
          .text("Loan Original Amount ($)");

    // draw x axis
    svg.append("g")
      .attr("class", "x axis")
      .attr("transform", "translate(0," + (height  + margin.top ) + ")")
      .call(xAxis)
      .append("text")             // text label for the x axis
        .attr("x", (width / 2) )
        .attr("y",  25 )
        .attr("dy", ".71em")
        .style("text-anchor", "middle")
        .style("font-size", "16px")
        .text("Income Range");
});}

// Returns a function to compute the interquartile range.
function iqr(k) {
  return function(d, i) {
    var q1 = d.quartiles[0],
        q3 = d.quartiles[2],
        iqr = (q3 - q1) * k,
        i = -1,
        j = d.length;
    while (d[++i] < q1 - iqr);
    while (d[--j] > q3 + iqr);
    return [i, j];
  };
}


//Similar draw function, different data

function draw_btn2(){d3.csv("loandata.csv", function(error, csv) {
    // using an array of arrays with
    // data[n][2]
    // where n = number of columns in the csv file
    // data[i][0] = name of the ith column
    // data[i][1] = array of values of ith column

    var data = [];
    data[0] = [];
    data[1] = [];
    data[2] = [];
    data[3] = [];
    data[4] = [];
    data[5] = [];

    // add more rows if your csv file has more columns

    // add here the header of the csv file
    data[0][0] = "$0";
    data[1][0] = "$1-24,999";
    data[2][0] = "$25,000-49,999";
    data[3][0] = "$50,000-74,999";
    data[4][0] = "75,000-99,999";
    data[5][0] = "100,000+";


    // add more rows if your csv file has more columns

    data[0][1] = [];
    data[1][1] = [];
    data[2][1] = [];
    data[3][1] = [];
    data[4][1] = [];
    data[5][1] = [];
    // add header
    data[0][0] = "$0";
    data[1][0] = "$1-24,999";
    data[2][0] = "$25,000-49,999";
    data[3][0] = "$50,000-74,999";
    data[4][0] = "$75,000-99,999";
    data[5][0] = "$100,000+";
    csv.forEach(function(x) {
      if (x.IsBorrowerHomeOwner =="TRUE"){
 if (x.IncomeRange == "$0") {
                        var v1 = Math.floor(x.LoanOriginalAmount);
                    }  if (x.IncomeRange == "$1-24,999") {
                        var v2 = Math.floor(x.LoanOriginalAmount);
                    } else if (x.IncomeRange == "$25,000-49,999") {
                        var v3 = Math.floor(x.LoanOriginalAmount);
                    } else if (x.IncomeRange == "$50,000-74,999") {
                        var v4 = Math.floor(x.LoanOriginalAmount);
                    } else if (x.IncomeRange == "$75,000-99,999") {
                        var v5 = Math.floor(x.LoanOriginalAmount);}
                        else if (x.IncomeRange == "$100,000+") {
                        var v6 = Math.floor(x.LoanOriginalAmount);}}
            // add more variables if your csv file has more columns

if (v1) {
                        data[0][1].push(v1);
                        if (v1 > max)
                            max = v1;
                        if (v1 < min)
                            min = v1;
                    }
                    if (v2) {
                        data[1][1].push(v2);
                        if (v2 > max)
                            max = v2;
                        if (v2 < min)
                            min = v2;
                    }
                    if (v3) {
                        data[2][1].push(v3);
                        if (v3 > max)
                            max = v3;
                        if (v3 < min)
                            min = v3;
                    }
                    if (v4) {
                        data[3][1].push(v4);
                        if (v4 > max)
                            max = v4;
                        if (v4 < min)
                            min = v4;
                    }
                    if (v5) {
                        data[4][1].push(v5);
                        if (v5 > max)
                            max = v5;
                        if (v5 < min)
                            min = v5;};
     if (v6) {
                        data[5][1].push(v6);
                        if (v6 > max)
                            max = v6;
                        if (v6 < min)
                            min = v6;}});


         // add more rows if your csv file has more columns




    var chart = d3.box()
        .whiskers(iqr(1.5))
        .height(height)
        .domain([min, max])
        .showLabels(labels);

    var svg = d3.select("body").select("svg")
        .attr("width", width + margin.left + margin.right)
        .attr("height", height + margin.top + margin.bottom)
        .attr("class", "box")
        .append("g")
        .attr("transform", "translate(" + margin.left + "," + margin.top + ")");








    // the x-axis
    var x = d3.scale.ordinal()
        .domain( data.map(function(d) { console.log(d); return d[0] } ) )
        .rangeRoundBands([0 , width], 0.7, 0.3);

    var xAxis = d3.svg.axis()
        .scale(x)
        .orient("bottom");

    // the y-axis
    var y = d3.scale.linear()
        .domain([min, max])
        .range([height + margin.top, 0 + margin.top]);

    var yAxis = d3.svg.axis()
    .scale(y)
    .orient("left");

    // draw the boxplots
    svg.selectAll(".box")
      .data(data)
      .enter().append("g")
        .attr("transform", function(d) { return "translate(" +  x(d[0])  + "," + margin.top + ")"; } )
      .call(chart.width(x.rangeBand()));



    // add a title
    svg.append("text")
        .attr("x", (width / 2))
        .attr("y", 0 )
        .attr("text-anchor", "middle")
        .style("font-size", "18px")
        //.style("text-decoration", "underline")
        .text("Loan Original Amount in different Income Range (Home Owner)");

     // draw y axis
    svg.append("g")
        .attr("class", "y axis")
        .call(yAxis)
        .append("text") // and text1
          .attr("transform", "rotate(-90)")
          .attr("y", 6)
          .attr("dy", ".71em")
          .style("text-anchor", "end")
          .style("font-size", "16px")
          .text("Loan Original Amount ($)");

    // draw x axis
    svg.append("g")
      .attr("class", "x axis")
      .attr("transform", "translate(0," + (height  + margin.top ) + ")")
      .call(xAxis)
      .append("text")             // text label for the x axis
        .attr("x", (width / 2) )
        .attr("y",  25 )
        .attr("dy", ".71em")
        .style("text-anchor", "middle")
        .style("font-size", "16px")
        .text("Income Range");
});}




</script>

<p> Try to focus on the median of the data, it is a good proxy to see the trend that loan amount is increasing with income range generally. Home Owner always have a larger median than Non- Home Owner. However, the $0 income range is a special case. It is interesting to note that people with no income even borrow more money than people have income in $1-24,999 group, that may be a dangerous loan!</p>

loandata.csv

,LoanOriginalAmount,IncomeRange,IsBorrowerHomeOwner
1,9425,"$25,000-49,999",TRUE
2,10000,"$50,000-74,999",FALSE
4,10000,"$25,000-49,999",TRUE
5,15000,"$100,000+",TRUE
6,15000,"$100,000+",TRUE
7,3000,"$25,000-49,999",FALSE
8,10000,"$25,000-49,999",FALSE
9,10000,"$25,000-49,999",TRUE
10,10000,"$25,000-49,999",TRUE
11,13500,"$75,000-99,999",FALSE
12,1000,"$1-24,999",FALSE
13,4000,"$25,000-49,999",FALSE
14,8500,"$50,000-74,999",TRUE
15,19330,"$100,000+",FALSE
16,4000,"$50,000-74,999",TRUE
17,4000,"$75,000-99,999",TRUE
19,15000,"$75,000-99,999",FALSE
20,6500,"$25,000-49,999",FALSE
21,14000,"$50,000-74,999",TRUE
22,3000,"$25,000-49,999",FALSE
23,4000,"$1-24,999",FALSE
24,2000,"$25,000-49,999",FALSE
25,4000,"$25,000-49,999",TRUE
26,4000,"$75,000-99,999",FALSE
27,4000,"$25,000-49,999",FALSE
28,4000,"$75,000-99,999",TRUE
29,10000,"$25,000-49,999",TRUE
30,35000,"$100,000+",TRUE
31,10000,"$25,000-49,999",FALSE
32,2000,"$25,000-49,999",FALSE
33,25000,"$75,000-99,999",TRUE
34,16000,"$100,000+",FALSE
35,10000,"$25,000-49,999",TRUE
36,9000,"$25,000-49,999",FALSE
37,10000,"$100,000+",TRUE
38,5000,"$25,000-49,999",TRUE
39,7000,"$50,000-74,999",TRUE
41,15000,"$100,000+",FALSE
42,13000,"$100,000+",TRUE
44,5000,"$50,000-74,999",FALSE
45,16000,"$100,000+",TRUE
46,25000,"$100,000+",TRUE
47,4000,"$25,000-49,999",FALSE
48,15000,"$50,000-74,999",TRUE
49,15000,"$100,000+",TRUE
50,25000,"$75,000-99,999",TRUE
51,10000,"$75,000-99,999",TRUE
52,2500,"$50,000-74,999",TRUE
53,7000,"$75,000-99,999",TRUE
54,3000,"$25,000-49,999",FALSE
55,18000,"$100,000+",TRUE
56,10000,"$25,000-49,999",FALSE
57,15000,"$50,000-74,999",FALSE
58,2000,"$100,000+",TRUE
59,8000,"$50,000-74,999",FALSE
60,7000,"$25,000-49,999",FALSE
61,25000,"$75,000-99,999",TRUE
62,2000,"$75,000-99,999",TRUE
63,23500,"$50,000-74,999",FALSE
65,5000,"$25,000-49,999",TRUE
66,15000,"$50,000-74,999",TRUE
68,3000,"$50,000-74,999",FALSE
69,1000,"$25,000-49,999",TRUE
70,9000,"$25,000-49,999",TRUE
71,10000,"$50,000-74,999",FALSE
72,2774,"$25,000-49,999",TRUE
73,4500,"$100,000+",FALSE
74,4000,"$50,000-74,999",TRUE
75,2500,"$50,000-74,999",TRUE
76,4000,"$50,000-74,999",FALSE
78,5600,"$75,000-99,999",TRUE
79,4800,$0,TRUE
80,22500,"$75,000-99,999",FALSE
81,4000,"$1-24,999",FALSE
82,25000,"$100,000+",FALSE
83,4000,"$50,000-74,999",FALSE
84,4000,"$25,000-49,999",FALSE
85,20000,"$50,000-74,999",TRUE
86,10000,"$25,000-49,999",TRUE
87,4500,"$50,000-74,999",TRUE
88,4000,"$25,000-49,999",TRUE
89,4000,"$75,000-99,999",TRUE
90,3500,"$75,000-99,999",FALSE
92,3500,"$25,000-49,999",FALSE
94,7000,"$50,000-74,999",TRUE
95,4000,"$50,000-74,999",TRUE
96,20000,"$100,000+",TRUE
97,15000,"$25,000-49,999",FALSE
98,2000,"$1-24,999",FALSE
99,4000,"$50,000-74,999",TRUE
100,10000,"$25,000-49,999",FALSE
102,2932,"$75,000-99,999",TRUE
103,10000,"$100,000+",TRUE
104,15000,"$50,000-74,999",TRUE
105,15000,"$50,000-74,999",FALSE
106,2000,"$25,000-49,999",FALSE
107,10000,"$50,000-74,999",FALSE
108,4000,"$25,000-49,999",FALSE
109,14000,$0,FALSE
110,2000,"$50,000-74,999",TRUE
111,3000,"$50,000-74,999",TRUE
112,4000,"$25,000-49,999",TRUE
113,4000,"$25,000-49,999",TRUE
114,3000,"$50,000-74,999",FALSE
115,13400,"$75,000-99,999",TRUE
116,2000,"$50,000-74,999",FALSE
117,15000,"$75,000-99,999",TRUE
118,2000,"$25,000-49,999",FALSE
119,6000,"$50,000-74,999",TRUE
120,15000,"$50,000-74,999",FALSE
121,10000,"$25,000-49,999",TRUE
122,4000,"$75,000-99,999",TRUE
124,12000,"$25,000-49,999",FALSE
125,15000,"$75,000-99,999",TRUE
127,14000,"$50,000-74,999",TRUE
128,2551,"$25,000-49,999",FALSE
130,10000,"$25,000-49,999",FALSE
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1761,3000,"$75,000-99,999",TRUE
1762,15000,"$100,000+",TRUE
1763,10000,"$50,000-74,999",TRUE
1764,15000,"$100,000+",TRUE
1765,3267,"$25,000-49,999",FALSE
1766,1500,"$25,000-49,999",FALSE
1767,5500,"$1-24,999",TRUE
1770,6500,"$75,000-99,999",FALSE
1771,4000,"$75,000-99,999",TRUE
1772,10000,"$25,000-49,999",FALSE
1773,25000,"$75,000-99,999",TRUE
1775,18456,"$100,000+",TRUE
1776,7500,"$25,000-49,999",TRUE
1777,6000,"$50,000-74,999",TRUE
1778,8500,"$50,000-74,999",FALSE
1779,6000,"$25,000-49,999",FALSE
1780,15000,"$25,000-49,999",FALSE
1781,3000,"$75,000-99,999",TRUE
1782,1200,"$1-24,999",FALSE
1783,1000,"$25,000-49,999",TRUE
1784,4000,"$25,000-49,999",FALSE
1785,4000,"$25,000-49,999",FALSE
1786,20000,"$50,000-74,999",TRUE
1787,10000,"$50,000-74,999",FALSE
1788,10000,"$25,000-49,999",TRUE
1789,20000,"$25,000-49,999",TRUE
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1791,1000,$0,TRUE
1792,20000,"$100,000+",TRUE
1793,3000,"$50,000-74,999",FALSE
1794,20000,"$75,000-99,999",TRUE
1795,4000,"$25,000-49,999",FALSE
1796,10000,"$75,000-99,999",TRUE
1797,4000,"$50,000-74,999",FALSE
1798,4500,"$50,000-74,999",FALSE
1799,7500,"$25,000-49,999",TRUE
1800,2780,"$1-24,999",FALSE
1801,11500,"$50,000-74,999",FALSE
1802,12500,"$75,000-99,999",TRUE
1803,2700,"$25,000-49,999",FALSE
1804,4000,"$50,000-74,999",TRUE
1805,4850,"$1-24,999",FALSE
1806,2000,"$50,000-74,999",TRUE
1808,12500,"$25,000-49,999",TRUE
1809,7500,"$100,000+",TRUE
1810,15000,"$25,000-49,999",FALSE
1811,10000,"$100,000+",TRUE
1812,15000,"$75,000-99,999",FALSE
1814,6000,"$50,000-74,999",TRUE
1815,11250,"$100,000+",TRUE
1816,2000,"$25,000-49,999",FALSE
1817,10000,"$25,000-49,999",FALSE
1818,3001,"$25,000-49,999",FALSE
1819,5000,"$100,000+",TRUE
1820,10000,"$25,000-49,999",TRUE
1821,15000,"$100,000+",TRUE
1822,2500,"$50,000-74,999",FALSE
1823,1250,$0,TRUE
1824,10000,"$75,000-99,999",TRUE
1825,1030,"$100,000+",TRUE
1826,15000,"$75,000-99,999",TRUE
1827,12000,"$50,000-74,999",TRUE
1828,5200,"$25,000-49,999",FALSE
1829,3000,"$50,000-74,999",FALSE
1831,5000,"$100,000+",TRUE
1832,7000,"$50,000-74,999",FALSE
1833,3900,"$25,000-49,999",FALSE
1834,7100,"$50,000-74,999",FALSE
1835,7900,"$50,000-74,999",TRUE
1836,4000,"$25,000-49,999",FALSE
1837,3100,"$25,000-49,999",FALSE
1838,8000,"$75,000-99,999",TRUE
1839,3100,"$50,000-74,999",TRUE
1840,1000,"$50,000-74,999",FALSE
1841,7000,"$25,000-49,999",TRUE
1842,2551,"$25,000-49,999",TRUE
1843,10000,"$100,000+",TRUE
1846,35000,"$100,000+",TRUE
1847,5000,"$75,000-99,999",TRUE
1849,1300,"$25,000-49,999",TRUE
1850,8500,"$50,000-74,999",FALSE
1851,2000,"$50,000-74,999",FALSE
1852,7500,"$25,000-49,999",FALSE
1853,15000,"$50,000-74,999",TRUE
1854,11500,"$75,000-99,999",TRUE
1855,8000,"$100,000+",TRUE
1856,20000,"$75,000-99,999",TRUE
1857,3500,"$50,000-74,999",TRUE
1858,4000,"$50,000-74,999",FALSE
1859,4000,"$1-24,999",FALSE
1861,9500,"$50,000-74,999",TRUE
1862,4000,"$25,000-49,999",FALSE
1863,2000,"$75,000-99,999",TRUE
1864,5000,"$25,000-49,999",FALSE
1865,3000,"$25,000-49,999",TRUE
1866,7000,"$75,000-99,999",TRUE
1867,3000,"$100,000+",TRUE
1868,8000,"$100,000+",TRUE
1869,10000,"$50,000-74,999",TRUE
1870,15000,"$75,000-99,999",TRUE
1871,10000,"$25,000-49,999",FALSE
1872,15000,"$75,000-99,999",TRUE
1873,2000,"$75,000-99,999",TRUE
1874,6001,"$25,000-49,999",FALSE
1875,12000,"$25,000-49,999",FALSE
1876,7000,"$50,000-74,999",TRUE
1877,1000,"$1-24,999",FALSE
1878,3000,"$1-24,999",FALSE
1879,2800,"$25,000-49,999",FALSE
1880,6000,"$25,000-49,999",TRUE
1881,2000,"$50,000-74,999",TRUE
1882,20000,"$50,000-74,999",TRUE
1883,4000,"$50,000-74,999",TRUE
1884,10000,"$100,000+",TRUE
1885,3500,"$50,000-74,999",FALSE
1886,5000,"$50,000-74,999",FALSE
1887,5000,"$25,000-49,999",FALSE
1888,4000,"$25,000-49,999",FALSE
1889,12000,"$25,000-49,999",FALSE
1890,4000,"$1-24,999",FALSE
1891,15000,"$100,000+",FALSE
1892,4000,"$25,000-49,999",FALSE
1893,20000,"$75,000-99,999",TRUE
1894,15000,"$50,000-74,999",TRUE
1895,2000,"$25,000-49,999",FALSE
1896,4000,"$25,000-49,999",FALSE
1897,20000,"$50,000-74,999",TRUE
1898,1000,"$100,000+",TRUE
1899,15000,"$100,000+",TRUE
1900,6000,"$25,000-49,999",TRUE
1901,12000,"$50,000-74,999",FALSE
1902,4000,"$25,000-49,999",FALSE
1903,3000,"$25,000-49,999",TRUE
1904,13000,"$50,000-74,999",FALSE
1905,3900,"$25,000-49,999",TRUE
1906,8000,"$100,000+",FALSE
1907,15000,"$25,000-49,999",FALSE
1910,4000,"$25,000-49,999",TRUE
1911,2000,"$25,000-49,999",FALSE
1912,20000,"$100,000+",FALSE
1913,4000,"$25,000-49,999",FALSE
1914,4000,"$25,000-49,999",TRUE
1915,8400,"$50,000-74,999",FALSE
1916,14000,"$100,000+",TRUE
1917,13000,"$1-24,999",FALSE
1918,3000,"$1-24,999",TRUE
1919,7500,"$75,000-99,999",FALSE
1920,1400,"$1-24,999",FALSE
1921,7500,"$50,000-74,999",TRUE
1922,13000,"$75,000-99,999",TRUE
1923,10500,"$25,000-49,999",TRUE
1924,3000,"$50,000-74,999",TRUE
1925,7000,"$50,000-74,999",TRUE
1927,4800,$0,TRUE
1928,6000,"$50,000-74,999",FALSE
1929,4000,"$100,000+",TRUE
1930,12000,"$100,000+",TRUE
1931,5000,"$1-24,999",FALSE
1934,2000,"$25,000-49,999",FALSE
1935,4000,"$100,000+",FALSE
1936,10000,"$50,000-74,999",TRUE
1937,5000,"$50,000-74,999",TRUE
1938,15000,"$50,000-74,999",FALSE
1939,15000,"$75,000-99,999",TRUE
1940,5500,"$50,000-74,999",FALSE
1941,10000,"$50,000-74,999",FALSE
1942,25000,"$75,000-99,999",TRUE
1943,2000,"$25,000-49,999",FALSE
1944,5500,"$50,000-74,999",FALSE
1945,3800,"$50,000-74,999",FALSE
1946,13000,"$50,000-74,999",TRUE
1947,10000,"$25,000-49,999",FALSE
1948,10000,"$25,000-49,999",TRUE
1949,15000,"$75,000-99,999",TRUE
1950,3500,"$25,000-49,999",FALSE
1951,15000,"$75,000-99,999",TRUE
1952,2000,"$25,000-49,999",FALSE
1953,5000,"$25,000-49,999",TRUE
1954,24000,"$75,000-99,999",FALSE
1955,3550,"$25,000-49,999",TRUE
1956,10000,"$25,000-49,999",FALSE
1957,4000,"$75,000-99,999",FALSE
1958,15000,"$50,000-74,999",TRUE
1960,13500,"$50,000-74,999",FALSE
1961,8000,"$75,000-99,999",TRUE
1962,15750,"$100,000+",TRUE
1963,14326,"$25,000-49,999",TRUE
1964,6500,"$50,000-74,999",FALSE
1965,4000,"$75,000-99,999",FALSE
1966,5100,"$50,000-74,999",FALSE
1967,9999,"$25,000-49,999",FALSE
1968,10000,"$50,000-74,999",TRUE
1969,20000,"$50,000-74,999",FALSE
1970,2000,"$25,000-49,999",TRUE
1971,8000,"$25,000-49,999",FALSE
1972,6000,"$50,000-74,999",TRUE
1973,4000,"$25,000-49,999",TRUE
1974,1400,"$25,000-49,999",FALSE
1975,3500,"$25,000-49,999",TRUE
1976,25000,"$100,000+",TRUE
1977,4500,"$75,000-99,999",TRUE
1978,1500,"$25,000-49,999",FALSE
1979,5000,"$75,000-99,999",TRUE
1980,3400,"$25,000-49,999",FALSE
1981,5000,"$25,000-49,999",TRUE
1982,15000,"$75,000-99,999",FALSE
1983,3000,"$25,000-49,999",FALSE
1984,15000,"$50,000-74,999",TRUE
1985,4000,"$75,000-99,999",FALSE
1986,21721,"$100,000+",FALSE
1987,15000,"$100,000+",TRUE
1988,4000,"$75,000-99,999",TRUE
1989,2000,"$25,000-49,999",FALSE
1990,15000,"$100,000+",TRUE
1991,11000,"$50,000-74,999",FALSE
1992,25000,"$100,000+",TRUE
1993,4000,"$1-24,999",FALSE
1994,5384,"$25,000-49,999",FALSE
1995,10000,"$50,000-74,999",TRUE
1996,4000,"$1-24,999",FALSE
1997,13500,"$100,000+",TRUE
1998,3000,"$25,000-49,999",TRUE
1999,2000,"$50,000-74,999",FALSE
2001,2700,"$50,000-74,999",FALSE
2002,7000,"$25,000-49,999",FALSE
2004,4000,"$25,000-49,999",FALSE
2005,4000,"$25,000-49,999",TRUE
2006,5000,"$50,000-74,999",FALSE
2007,10000,"$75,000-99,999",FALSE
2008,12000,"$25,000-49,999",FALSE
2009,3400,"$50,000-74,999",TRUE
2011,15000,"$50,000-74,999",FALSE
2012,4500,"$25,000-49,999",FALSE
2013,6000,"$1-24,999",FALSE
2014,15000,"$50,000-74,999",TRUE
2015,4000,"$25,000-49,999",FALSE
2016,7500,"$100,000+",TRUE
2017,7500,"$75,000-99,999",TRUE
2018,11500,"$50,000-74,999",TRUE
2019,4000,"$25,000-49,999",FALSE
2020,10000,"$25,000-49,999",TRUE
2021,10000,"$25,000-49,999",TRUE
2022,15000,"$25,000-49,999",TRUE
2023,4250,"$50,000-74,999",TRUE
2024,2500,"$75,000-99,999",TRUE
2025,25000,"$50,000-74,999",FALSE
2026,1800,"$25,000-49,999",TRUE
2027,5000,"$50,000-74,999",FALSE
2028,3000,"$50,000-74,999",FALSE
2029,4000,"$75,000-99,999",FALSE
2032,12000,"$75,000-99,999",FALSE
2033,15000,"$75,000-99,999",TRUE
2034,2000,"$25,000-49,999",TRUE
2035,15000,"$50,000-74,999",TRUE
2036,2000,"$75,000-99,999",TRUE
2037,3500,"$25,000-49,999",TRUE
2038,8000,"$75,000-99,999",TRUE
2039,10000,"$50,000-74,999",FALSE
2040,1000,"$1-24,999",FALSE
2042,10000,"$100,000+",FALSE
2043,7500,"$50,000-74,999",FALSE
2044,15000,"$25,000-49,999",TRUE
2045,3000,"$25,000-49,999",FALSE
2046,8000,"$50,000-74,999",FALSE
2047,15001,"$50,000-74,999",TRUE
2048,9000,"$25,000-49,999",TRUE
2049,13000,"$100,000+",TRUE
2052,10000,"$25,000-49,999",TRUE
2053,5000,"$50,000-74,999",FALSE
2055,14000,"$25,000-49,999",FALSE
2056,4000,"$50,000-74,999",FALSE
2057,15000,"$50,000-74,999",FALSE
2059,9000,"$50,000-74,999",TRUE
2060,10000,"$25,000-49,999",FALSE
2061,2500,"$75,000-99,999",TRUE
2062,8000,"$100,000+",TRUE
2063,7000,"$25,000-49,999",FALSE
2064,12300,"$75,000-99,999",FALSE
2065,7000,$0,FALSE
2066,13000,"$25,000-49,999",TRUE
2067,5000,"$25,000-49,999",TRUE
2068,4000,"$25,000-49,999",FALSE
2069,4000,"$25,000-49,999",TRUE
2070,13323,"$50,000-74,999",FALSE
2071,15000,"$50,000-74,999",TRUE
2072,7000,"$25,000-49,999",FALSE
2073,5000,"$75,000-99,999",FALSE
2074,13000,"$75,000-99,999",FALSE
2075,4000,"$75,000-99,999",TRUE
2076,1600,"$75,000-99,999",FALSE
2077,4500,"$1-24,999",FALSE
2078,4000,"$100,000+",TRUE
2079,8000,"$100,000+",FALSE
2080,3200,"$50,000-74,999",FALSE
2081,15000,"$50,000-74,999",TRUE
2082,10000,"$25,000-49,999",TRUE
2083,4000,"$75,000-99,999",TRUE
2084,4000,"$50,000-74,999",TRUE
2085,4000,"$50,000-74,999",TRUE
2086,3000,"$50,000-74,999",FALSE
2087,2000,"$25,000-49,999",TRUE
2088,4000,"$75,000-99,999",TRUE
2089,2000,"$50,000-74,999",FALSE
2090,2500,"$1-24,999",FALSE
2091,10000,"$75,000-99,999",TRUE
2092,7000,"$100,000+",TRUE
2093,15000,"$100,000+",TRUE
2094,10000,"$100,000+",TRUE
2095,9800,"$75,000-99,999",FALSE
2096,20000,"$75,000-99,999",FALSE
2097,15000,"$100,000+",TRUE
2098,25000,"$100,000+",TRUE
2099,4000,"$25,000-49,999",TRUE
2100,8500,"$25,000-49,999",FALSE
2101,1500,"$1-24,999",FALSE
2102,15000,"$50,000-74,999",FALSE
2103,4000,"$25,000-49,999",TRUE
2104,5000,"$25,000-49,999",FALSE
2105,3500,"$25,000-49,999",FALSE
2108,2000,"$25,000-49,999",FALSE
2109,17500,"$75,000-99,999",FALSE
2110,10000,"$75,000-99,999",TRUE
2111,15000,"$50,000-74,999",FALSE
2112,8500,"$25,000-49,999",TRUE
2113,9000,"$100,000+",TRUE
2114,15000,"$100,000+",FALSE
2115,2500,"$25,000-49,999",FALSE
2116,15000,"$50,000-74,999",TRUE
2117,1000,"$100,000+",TRUE
2118,10000,"$50,000-74,999",TRUE
2119,7500,"$25,000-49,999",TRUE
2120,7000,"$25,000-49,999",TRUE
2121,9000,"$25,000-49,999",FALSE
2122,1500,"$25,000-49,999",TRUE
2123,20000,"$100,000+",TRUE
2124,4000,"$1-24,999",FALSE
2125,10000,"$25,000-49,999",FALSE
2126,5000,"$25,000-49,999",TRUE
2127,35000,"$100,000+",TRUE
2128,10000,"$50,000-74,999",TRUE
2129,4000,"$25,000-49,999",FALSE
2130,1250,"$25,000-49,999",TRUE
2131,10000,"$25,000-49,999",FALSE
2132,8000,"$100,000+",TRUE
2133,4250,"$1-24,999",TRUE
2134,4000,"$25,000-49,999",TRUE
2135,7500,"$50,000-74,999",TRUE
2136,15000,"$100,000+",TRUE
2137,8000,"$100,000+",TRUE
2138,2000,"$25,000-49,999",FALSE
2139,1500,"$1-24,999",TRUE
2140,5400,"$25,000-49,999",FALSE
2141,15000,"$75,000-99,999",TRUE
2142,15000,"$50,000-74,999",TRUE
2143,8000,"$25,000-49,999",TRUE
2144,4000,"$25,000-49,999",FALSE
2145,3000,"$50,000-74,999",FALSE
2146,3000,"$100,000+",TRUE
2148,7875,"$50,000-74,999",TRUE
2149,2500,"$100,000+",FALSE
2150,34999,"$100,000+",TRUE
2151,4000,"$50,000-74,999",TRUE
2152,9000,"$25,000-49,999",TRUE
2153,7500,"$50,000-74,999",FALSE
2154,6500,"$25,000-49,999",TRUE
2155,2551,"$25,000-49,999",FALSE
2156,13500,"$100,000+",TRUE
2157,4000,"$100,000+",TRUE
2158,4000,"$100,000+",TRUE
2159,10000,"$75,000-99,999",FALSE
2160,9900,"$50,000-74,999",FALSE
2161,7500,"$25,000-49,999",FALSE
2162,5000,"$75,000-99,999",FALSE
2163,2000,"$25,000-49,999",TRUE
2164,8000,"$100,000+",TRUE
2165,13000,"$100,000+",TRUE
2166,3800,"$50,000-74,999",TRUE
2167,4000,"$25,000-49,999",FALSE
2168,4000,"$25,000-49,999",TRUE
2169,16000,"$25,000-49,999",TRUE
2170,15000,"$75,000-99,999",FALSE
2171,10000,"$25,000-49,999",TRUE
2172,3000,"$75,000-99,999",FALSE
2173,10000,"$100,000+",TRUE
2174,6000,"$50,000-74,999",FALSE
2175,15000,"$50,000-74,999",TRUE
2176,6000,"$75,000-99,999",TRUE
2177,10000,"$25,000-49,999",FALSE
2178,2250,"$50,000-74,999",FALSE
2179,8500,"$50,000-74,999",TRUE
2180,16000,"$100,000+",TRUE
2181,8430,"$25,000-49,999",FALSE
2182,4500,"$100,000+",TRUE
2183,4000,"$1-24,999",FALSE
2184,10000,"$25,000-49,999",TRUE
2186,1000,"$1-24,999",TRUE
2187,6000,"$25,000-49,999",FALSE
2188,15000,"$100,000+",TRUE
2189,27500,"$100,000+",FALSE
2191,7500,"$75,000-99,999",FALSE
2192,6000,"$50,000-74,999",TRUE
2193,8500,"$100,000+",TRUE
2194,4000,"$25,000-49,999",TRUE
2195,15000,"$100,000+",TRUE
2196,7350,"$25,000-49,999",FALSE
2197,2000,"$1-24,999",FALSE
2198,4000,"$100,000+",TRUE
2199,10000,"$50,000-74,999",FALSE
2201,32500,"$100,000+",TRUE
2202,1500,"$25,000-49,999",FALSE
2203,6000,"$50,000-74,999",FALSE
2204,3000,"$50,000-74,999",TRUE
2206,26000,"$100,000+",TRUE
2207,4000,"$25,000-49,999",TRUE
2208,3500,"$75,000-99,999",TRUE
2209,7500,"$100,000+",TRUE
2210,15000,"$50,000-74,999",TRUE
2211,20000,"$100,000+",TRUE
2212,7000,"$25,000-49,999",FALSE
2213,15000,"$100,000+",TRUE
2214,4000,"$50,000-74,999",TRUE
2215,10000,"$50,000-74,999",TRUE
2216,10000,"$50,000-74,999",FALSE
2217,15000,"$50,000-74,999",TRUE
2219,4000,"$25,000-49,999",FALSE
2220,1400,"$1-24,999",FALSE
2221,6500,"$25,000-49,999",FALSE
2222,15000,"$100,000+",TRUE
2223,1000,"$25,000-49,999",FALSE
2224,13500,"$50,000-74,999",FALSE
2225,4000,"$50,000-74,999",TRUE
2226,15000,"$100,000+",FALSE
2227,6500,"$50,000-74,999",FALSE
2228,15000,"$75,000-99,999",TRUE
2229,12000,"$50,000-74,999",TRUE
2230,8500,"$50,000-74,999",TRUE
2231,7500,"$50,000-74,999",TRUE
2232,15000,"$25,000-49,999",FALSE
2233,29765,"$100,000+",TRUE
2234,8000,"$100,000+",FALSE
2235,10000,"$50,000-74,999",TRUE
2236,4600,"$25,000-49,999",FALSE
2237,10000,"$75,000-99,999",FALSE
2239,9500,"$25,000-49,999",FALSE
2240,1000,"$25,000-49,999",FALSE
2241,15000,"$50,000-74,999",TRUE
2242,3500,"$25,000-49,999",FALSE
2243,17000,"$100,000+",TRUE
2244,10000,"$50,000-74,999",TRUE
2245,5500,"$25,000-49,999",FALSE
2246,3500,"$75,000-99,999",TRUE
2247,3500,"$25,000-49,999",FALSE
2248,7000,"$75,000-99,999",TRUE
2249,25000,"$100,000+",FALSE
2250,4500,"$25,000-49,999",FALSE
2252,5000,"$25,000-49,999",TRUE
2253,17500,"$100,000+",TRUE
2254,4000,"$25,000-49,999",TRUE
2256,20000,"$100,000+",FALSE
2257,25000,"$100,000+",TRUE
2258,12000,"$50,000-74,999",FALSE
2259,4000,"$75,000-99,999",TRUE
2260,1000,"$1-24,999",FALSE
2261,5000,"$25,000-49,999",TRUE
2262,4000,"$25,000-49,999",FALSE
2263,10000,"$50,000-74,999",FALSE
2264,3500,"$1-24,999",FALSE
2265,12000,"$75,000-99,999",FALSE
2266,25000,"$100,000+",TRUE
2267,2000,"$25,000-49,999",FALSE
2268,15000,"$75,000-99,999",TRUE
2269,3000,"$25,000-49,999",TRUE
2270,10000,"$25,000-49,999",FALSE
2271,5000,"$25,000-49,999",FALSE
2274,7000,"$75,000-99,999",FALSE
2275,13500,"$50,000-74,999",FALSE
2276,4000,"$25,000-49,999",TRUE
2277,6000,"$25,000-49,999",TRUE
2278,3000,"$50,000-74,999",FALSE
2279,1200,"$75,000-99,999",TRUE
2280,16000,"$25,000-49,999",FALSE
2281,2900,"$75,000-99,999",TRUE
2282,2200,"$1-24,999",FALSE
2283,2551,"$25,000-49,999",FALSE
2284,15000,"$75,000-99,999",FALSE
2285,14500,"$75,000-99,999",FALSE
2286,25000,"$100,000+",TRUE
2287,9800,"$50,000-74,999",TRUE
2288,20000,"$50,000-74,999",TRUE
2289,35000,"$100,000+",TRUE
2290,13000,"$100,000+",TRUE
2291,4000,"$75,000-99,999",FALSE
2292,10000,"$100,000+",TRUE
2293,3000,"$25,000-49,999",FALSE
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2527,4000,"$75,000-99,999",TRUE
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2531,3500,"$75,000-99,999",TRUE
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2535,3700,"$75,000-99,999",TRUE
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2537,10000,"$25,000-49,999",TRUE
2538,10000,"$25,000-49,999",TRUE
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2584,20000,$0,TRUE
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2586,4000,"$100,000+",FALSE
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2597,15000,"$25,000-49,999",TRUE
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2601,4750,"$1-24,999",FALSE
2602,5177,"$50,000-74,999",TRUE
2603,6500,"$25,000-49,999",TRUE
2604,19000,"$75,000-99,999",TRUE
2605,4500,"$25,000-49,999",TRUE
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2607,10000,"$25,000-49,999",FALSE
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2629,10000,"$50,000-74,999",TRUE
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2631,7000,"$75,000-99,999",TRUE
2633,7500,"$25,000-49,999",TRUE
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2635,10000,"$25,000-49,999",TRUE
2636,10000,"$25,000-49,999",TRUE
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2644,1025,"$25,000-49,999",TRUE
2645,20000,"$50,000-74,999",FALSE
2647,4000,"$25,000-49,999",FALSE
2648,2000,"$25,000-49,999",TRUE
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2659,3500,"$1-24,999",TRUE
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2699,15000,"$50,000-74,999",TRUE
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2708,4000,"$50,000-74,999",TRUE
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2725,12000,"$50,000-74,999",FALSE
2726,4000,"$75,000-99,999",TRUE
2727,10000,"$75,000-99,999",TRUE
2728,10000,"$100,000+",TRUE
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2735,15000,"$25,000-49,999",TRUE
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2753,7000,"$50,000-74,999",TRUE
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2756,7000,"$25,000-49,999",FALSE
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2788,2200,"$50,000-74,999",TRUE
2790,3500,"$1-24,999",FALSE
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2798,25000,"$75,000-99,999",TRUE
2799,10000,"$50,000-74,999",TRUE
2800,1000,"$25,000-49,999",FALSE
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2803,13000,"$50,000-74,999",TRUE
2804,16000,"$100,000+",FALSE
2805,12000,"$75,000-99,999",TRUE
2806,15000,"$75,000-99,999",FALSE
2807,1000,"$1-24,999",FALSE
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2809,7000,"$75,000-99,999",TRUE
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2990,12000,"$50,000-74,999",FALSE
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2992,11631,"$75,000-99,999",TRUE
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2994,1000,"$25,000-49,999",FALSE
2995,15000,"$75,000-99,999",TRUE
2996,25000,"$50,000-74,999",TRUE
2998,4000,"$50,000-74,999",TRUE
2999,10000,"$50,000-74,999",TRUE
3000,10000,"$100,000+",TRUE
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3021,1000,"$50,000-74,999",FALSE
3022,1500,"$25,000-49,999",TRUE
3023,3000,"$75,000-99,999",TRUE
3024,2939,"$50,000-74,999",FALSE
3025,10000,"$25,000-49,999",TRUE
3026,5000,"$50,000-74,999",TRUE
3027,13000,"$50,000-74,999",TRUE
3028,4000,"$25,000-49,999",FALSE
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3030,4000,"$50,000-74,999",FALSE
3032,10000,"$50,000-74,999",TRUE
3033,12000,"$50,000-74,999",TRUE
3034,7500,"$50,000-74,999",TRUE
3035,15000,"$50,000-74,999",TRUE
3036,3500,"$25,000-49,999",TRUE
3037,4000,"$100,000+",TRUE
3038,3000,"$50,000-74,999",FALSE
3039,10000,"$25,000-49,999",FALSE
3040,1500,"$25,000-49,999",TRUE
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3042,25000,"$75,000-99,999",FALSE
3043,25000,"$100,000+",TRUE
3044,9000,"$100,000+",TRUE
3045,10000,"$50,000-74,999",TRUE
3046,25000,"$100,000+",TRUE
3047,2500,"$50,000-74,999",TRUE
3048,7000,"$50,000-74,999",FALSE
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3050,3000,"$50,000-74,999",TRUE
3051,15000,"$75,000-99,999",FALSE
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3054,25000,"$100,000+",TRUE
3055,10000,"$50,000-74,999",TRUE
3056,15000,$0,TRUE
3058,4000,"$50,000-74,999",FALSE
3060,5000,"$1-24,999",FALSE
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3064,10000,"$50,000-74,999",FALSE
3065,7000,"$50,000-74,999",TRUE
3066,2000,"$25,000-49,999",TRUE
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3068,15000,"$100,000+",TRUE
3069,8000,"$50,000-74,999",FALSE
3070,15000,"$50,000-74,999",FALSE
3071,4900,"$1-24,999",FALSE
3072,15000,"$100,000+",TRUE
3073,20000,"$100,000+",TRUE
3074,1000,"$50,000-74,999",TRUE
3075,15000,"$50,000-74,999",TRUE
3076,4000,"$75,000-99,999",TRUE
3077,4000,"$100,000+",TRUE
3078,25000,"$100,000+",TRUE
3079,10350,"$50,000-74,999",TRUE
3080,20000,"$100,000+",TRUE
3081,1800,"$25,000-49,999",FALSE
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3083,4000,"$50,000-74,999",TRUE
3084,2000,"$50,000-74,999",FALSE
3085,6000,"$50,000-74,999",TRUE
3086,10000,"$25,000-49,999",FALSE
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3090,4000,"$50,000-74,999",TRUE
3091,5000,"$1-24,999",FALSE
3092,12000,"$75,000-99,999",FALSE
3093,3200,"$50,000-74,999",FALSE
3094,8000,"$50,000-74,999",TRUE
3095,1200,"$1-24,999",TRUE
3096,2000,"$50,000-74,999",FALSE
3097,2000,"$1-24,999",TRUE
3098,10000,"$50,000-74,999",TRUE
3099,4000,"$50,000-74,999",TRUE
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3101,6000,"$75,000-99,999",TRUE
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3107,7500,"$25,000-49,999",FALSE
3108,3000,"$1-24,999",TRUE
3109,25000,"$25,000-49,999",TRUE
3110,4000,"$1-24,999",TRUE
3111,6750,"$75,000-99,999",FALSE
3112,12500,"$50,000-74,999",TRUE
3113,15000,"$25,000-49,999",TRUE
3115,2500,"$50,000-74,999",TRUE
3116,3500,"$25,000-49,999",FALSE
3117,12000,"$25,000-49,999",FALSE
3118,5000,"$50,000-74,999",TRUE
3119,5000,"$25,000-49,999",TRUE
3120,3500,"$25,000-49,999",FALSE
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3122,3000,"$1-24,999",FALSE
3123,8000,"$50,000-74,999",TRUE
3124,6000,"$50,000-74,999",TRUE
3125,13000,"$100,000+",TRUE
3126,15000,"$25,000-49,999",FALSE
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3130,7000,"$50,000-74,999",TRUE
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3134,15000,"$75,000-99,999",TRUE
3135,7500,"$75,000-99,999",FALSE
3136,15000,"$50,000-74,999",TRUE
3137,4000,"$25,000-49,999",FALSE
3138,10000,"$25,000-49,999",FALSE
3139,15000,"$50,000-74,999",TRUE
3140,12000,"$25,000-49,999",TRUE
3141,2000,"$25,000-49,999",TRUE
3142,10000,"$25,000-49,999",TRUE
3143,1000,"$25,000-49,999",TRUE
3144,4000,"$50,000-74,999",FALSE
3145,10000,"$50,000-74,999",TRUE
3146,2000,"$1-24,999",FALSE
3147,3500,"$25,000-49,999",TRUE
3148,25000,"$100,000+",TRUE
3149,15000,"$100,000+",TRUE
3150,15000,"$100,000+",FALSE
3151,5000,"$50,000-74,999",TRUE
3152,1000,$0,FALSE
3154,10000,"$25,000-49,999",TRUE
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3156,4500,"$50,000-74,999",TRUE
3157,15000,"$50,000-74,999",FALSE
3158,5000,"$100,000+",TRUE
3159,1000,"$25,000-49,999",TRUE
3160,4000,"$25,000-49,999",TRUE
3161,7767,"$25,000-49,999",FALSE
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3163,12000,"$75,000-99,999",TRUE
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3165,15000,"$25,000-49,999",TRUE
3166,7000,"$25,000-49,999",TRUE
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3168,4000,"$50,000-74,999",FALSE
3169,12000,"$50,000-74,999",TRUE
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3171,3493,"$50,000-74,999",FALSE
3172,9000,"$25,000-49,999",TRUE
3173,7000,"$25,000-49,999",FALSE
3175,2000,"$1-24,999",FALSE
3176,2800,"$50,000-74,999",TRUE
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3179,3000,"$50,000-74,999",TRUE
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3182,15000,"$75,000-99,999",TRUE
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3184,5000,"$25,000-49,999",FALSE
3185,4000,"$25,000-49,999",FALSE
3186,15000,"$100,000+",FALSE
3187,6000,"$50,000-74,999",FALSE
3188,3500,"$50,000-74,999",TRUE
3189,3500,"$50,000-74,999",TRUE
3190,35000,"$100,000+",TRUE
3191,15000,"$50,000-74,999",TRUE
3194,3500,"$1-24,999",FALSE
3195,9200,"$100,000+",TRUE
3196,7500,"$50,000-74,999",TRUE
3197,2000,"$25,000-49,999",FALSE
3198,4000,"$25,000-49,999",FALSE
3199,2000,"$25,000-49,999",TRUE
3200,10000,"$25,000-49,999",TRUE
3202,10000,"$25,000-49,999",TRUE
3203,2000,"$1-24,999",FALSE
3204,3842,"$25,000-49,999",TRUE
3205,10000,"$75,000-99,999",TRUE
3206,3000,"$1-24,999",FALSE
3207,2000,"$25,000-49,999",FALSE
3210,4000,"$25,000-49,999",TRUE
3211,6000,"$25,000-49,999",TRUE
3212,15000,"$50,000-74,999",TRUE
3213,3000,"$50,000-74,999",FALSE
3214,10000,"$25,000-49,999",FALSE
3215,1200,"$25,000-49,999",FALSE
3216,2000,"$50,000-74,999",FALSE
3217,4000,"$25,000-49,999",FALSE
3218,5000,"$100,000+",TRUE
3219,8500,"$75,000-99,999",TRUE
3220,7000,"$75,000-99,999",FALSE
3221,5000,"$50,000-74,999",FALSE
3222,4000,"$50,000-74,999",TRUE
3223,10000,"$50,000-74,999",TRUE
3224,8000,"$25,000-49,999",FALSE
3225,8000,"$25,000-49,999",TRUE
3226,15000,"$75,000-99,999",TRUE
3227,8000,"$25,000-49,999",TRUE
3228,11293,"$50,000-74,999",FALSE
3229,11000,"$1-24,999",FALSE
3230,10000,"$75,000-99,999",TRUE
3231,25000,"$75,000-99,999",TRUE
3232,12575,"$50,000-74,999",FALSE
3233,15000,"$75,000-99,999",FALSE
3234,1000,"$25,000-49,999",FALSE
3236,2800,"$50,000-74,999",FALSE
3237,4000,"$75,000-99,999",TRUE
3238,11000,"$50,000-74,999",FALSE
3239,2000,"$1-24,999",FALSE
3240,12500,"$50,000-74,999",FALSE
3241,3000,"$50,000-74,999",TRUE
3242,2000,"$1-24,999",FALSE
3243,2500,"$50,000-74,999",TRUE
3244,3000,"$1-24,999",TRUE
3245,6500,"$50,000-74,999",FALSE
3246,4000,"$1-24,999",FALSE
3247,2000,"$100,000+",TRUE
3248,10000,"$25,000-49,999",TRUE
3249,6500,"$25,000-49,999",FALSE
3250,4000,"$100,000+",TRUE
3253,15000,"$50,000-74,999",FALSE
3254,23750,"$50,000-74,999",TRUE
3255,20000,"$100,000+",FALSE
3256,4000,"$25,000-49,999",TRUE
3258,4000,"$100,000+",TRUE
3259,15000,"$50,000-74,999",TRUE
3260,7000,"$100,000+",TRUE
3261,2000,"$25,000-49,999",FALSE
3262,20000,"$25,000-49,999",FALSE
3263,10000,"$50,000-74,999",TRUE
3264,4000,"$25,000-49,999",TRUE
3265,10000,"$25,000-49,999",FALSE
3267,1000,"$25,000-49,999",FALSE
3268,5000,"$75,000-99,999",TRUE
3270,4000,"$25,000-49,999",FALSE
3271,5000,"$50,000-74,999",TRUE
3272,13000,"$25,000-49,999",TRUE
3273,13000,"$50,000-74,999",TRUE
3274,3000,"$25,000-49,999",FALSE
3275,2500,"$100,000+",TRUE
3276,7500,"$50,000-74,999",FALSE
3277,5000,"$1-24,999",FALSE
3279,7500,"$100,000+",TRUE
3280,4500,"$25,000-49,999",FALSE
3281,10000,"$25,000-49,999",FALSE
3282,5000,"$75,000-99,999",FALSE
3283,10000,"$25,000-49,999",FALSE
3284,13000,"$100,000+",FALSE
3286,6000,"$25,000-49,999",TRUE
3287,25000,"$75,000-99,999",TRUE
3288,13000,"$75,000-99,999",TRUE
3289,15000,"$100,000+",FALSE
3290,13000,"$25,000-49,999",FALSE
3291,3000,"$25,000-49,999",TRUE
3292,4000,"$100,000+",TRUE
3293,10000,"$25,000-49,999",TRUE
3294,25000,"$25,000-49,999",FALSE
3295,15000,"$50,000-74,999",TRUE
3296,2060,"$50,000-74,999",FALSE
3297,15000,"$75,000-99,999",TRUE
3298,3500,"$1-24,999",FALSE
3299,15000,"$75,000-99,999",TRUE
3300,3500,"$50,000-74,999",TRUE
3301,4000,"$1-24,999",FALSE
3302,10000,"$25,000-49,999",TRUE
3303,1600,"$75,000-99,999",FALSE
3304,4000,"$50,000-74,999",FALSE
3305,7000,"$50,000-74,999",FALSE
3306,15000,"$50,000-74,999",TRUE
3307,1500,"$100,000+",TRUE
3308,4200,"$25,000-49,999",FALSE
3309,2000,"$50,000-74,999",TRUE
3310,2000,"$75,000-99,999",TRUE
3311,3500,"$75,000-99,999",FALSE
3312,3800,"$75,000-99,999",TRUE
3313,35000,"$100,000+",TRUE
3315,5000,"$75,000-99,999",FALSE
3316,5000,"$75,000-99,999",FALSE
3317,5000,"$25,000-49,999",TRUE
3318,1000,"$50,000-74,999",TRUE
3319,3500,"$50,000-74,999",TRUE
3320,10280,"$100,000+",TRUE
3321,13000,"$100,000+",TRUE
3322,10000,"$25,000-49,999",FALSE
3323,5000,"$50,000-74,999",FALSE
3324,4000,"$25,000-49,999",FALSE
3326,9000,"$100,000+",TRUE
3327,15000,"$100,000+",FALSE
3328,12000,"$100,000+",FALSE
3329,15000,"$75,000-99,999",TRUE
3331,10000,"$25,000-49,999",TRUE
3332,2500,"$1-24,999",FALSE
3333,10000,"$50,000-74,999",TRUE
3334,4000,"$50,000-74,999",TRUE
3335,8500,"$25,000-49,999",FALSE
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4110,15000,"$25,000-49,999",TRUE
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4213,5000,"$50,000-74,999",FALSE
4214,7000,"$25,000-49,999",FALSE
4215,5000,"$50,000-74,999",TRUE
4216,12500,"$25,000-49,999",TRUE
4217,12000,"$100,000+",TRUE
4218,5000,"$50,000-74,999",TRUE
4219,20000,"$50,000-74,999",TRUE
4220,18000,"$100,000+",TRUE
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4224,25000,"$100,000+",TRUE
4225,4000,"$75,000-99,999",TRUE
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4230,2500,"$25,000-49,999",TRUE
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4232,4000,"$50,000-74,999",TRUE
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4264,2400,"$25,000-49,999",FALSE
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4288,10000,"$25,000-49,999",TRUE
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4297,7000,"$25,000-49,999",TRUE
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4299,10000,"$25,000-49,999",TRUE
4300,15000,"$50,000-74,999",TRUE
4301,30000,"$100,000+",TRUE
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4307,7500,"$50,000-74,999",TRUE
4308,15000,"$50,000-74,999",TRUE
4309,12000,"$50,000-74,999",FALSE
4310,5000,"$25,000-49,999",FALSE
4311,7500,"$100,000+",TRUE
4313,12900,"$100,000+",TRUE
4314,3000,"$100,000+",FALSE
4315,14500,"$50,000-74,999",TRUE
4316,15000,"$50,000-74,999",FALSE
4317,5000,"$50,000-74,999",TRUE
4318,8000,"$100,000+",FALSE
4319,10000,"$25,000-49,999",FALSE
4320,15000,"$100,000+",TRUE
4321,15000,"$50,000-74,999",TRUE
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4324,7500,"$25,000-49,999",FALSE
4325,2000,"$25,000-49,999",TRUE
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4327,4000,"$75,000-99,999",TRUE
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4330,3500,"$25,000-49,999",TRUE
4332,4500,"$75,000-99,999",TRUE
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4340,11000,"$100,000+",TRUE
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4351,10000,"$25,000-49,999",TRUE
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4356,10000,"$75,000-99,999",TRUE
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4360,8000,"$25,000-49,999",TRUE
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4362,15000,"$50,000-74,999",TRUE
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4367,20000,"$100,000+",TRUE
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4370,4000,"$50,000-74,999",TRUE
4371,20000,"$75,000-99,999",TRUE
4373,3000,"$25,000-49,999",FALSE
4374,4000,"$1-24,999",FALSE
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4884,7000,"$100,000+",TRUE
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5029,12000,"$100,000+",TRUE
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5032,1500,"$25,000-49,999",FALSE
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5101,15000,"$100,000+",TRUE
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5112,10000,"$50,000-74,999",TRUE
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5119,7500,"$100,000+",FALSE
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5146,3000,"$25,000-49,999",TRUE
5147,11500,$0,TRUE
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5190,15000,"$50,000-74,999",TRUE
5191,10000,"$75,000-99,999",TRUE
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5200,7000,"$50,000-74,999",TRUE
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5206,15000,"$100,000+",TRUE
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5210,2000,"$75,000-99,999",TRUE
5211,10000,"$75,000-99,999",TRUE
5212,1700,"$25,000-49,999",FALSE
5214,21000,"$75,000-99,999",FALSE
5215,3000,"$50,000-74,999",FALSE
5216,4000,"$100,000+",TRUE
5217,4000,"$25,000-49,999",FALSE
5218,3600,"$25,000-49,999",TRUE
5219,2500,"$75,000-99,999",TRUE
5220,4999,"$100,000+",TRUE
5221,2000,"$75,000-99,999",TRUE
5222,7500,"$100,000+",TRUE
5223,4000,"$50,000-74,999",FALSE
5224,17500,"$100,000+",TRUE
5226,5000,"$75,000-99,999",TRUE
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5228,3500,"$50,000-74,999",FALSE
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5230,11500,"$75,000-99,999",TRUE
5231,7200,"$25,000-49,999",FALSE
5232,15000,"$75,000-99,999",TRUE
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5235,7500,"$50,000-74,999",TRUE
5236,5964,"$25,000-49,999",FALSE
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5248,7500,"$50,000-74,999",TRUE
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5253,15000,"$75,000-99,999",TRUE
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5255,2000,"$50,000-74,999",TRUE
5256,19000,"$100,000+",TRUE
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5260,25000,"$75,000-99,999",FALSE
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5263,20000,"$50,000-74,999",TRUE
5264,4000,"$25,000-49,999",TRUE
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5267,20000,"$50,000-74,999",TRUE
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5279,5000,"$50,000-74,999",TRUE
5280,4500,"$100,000+",TRUE
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5290,17500,"$75,000-99,999",TRUE
5291,7000,"$25,000-49,999",TRUE
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5299,18000,"$75,000-99,999",FALSE
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5301,5000,"$25,000-49,999",FALSE
5303,6000,"$75,000-99,999",TRUE
5304,5000,"$50,000-74,999",TRUE
5305,12500,"$100,000+",TRUE
5306,2000,"$50,000-74,999",TRUE
5307,17000,"$25,000-49,999",FALSE
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5309,14000,"$50,000-74,999",TRUE
5310,7500,"$75,000-99,999",TRUE
5311,9000,"$25,000-49,999",TRUE
5312,2000,"$25,000-49,999",FALSE
5313,22000,"$25,000-49,999",TRUE
5314,15000,"$75,000-99,999",TRUE
5315,6500,"$50,000-74,999",TRUE
5316,25000,"$50,000-74,999",TRUE
5317,8554,"$50,000-74,999",TRUE
5318,25000,"$75,000-99,999",TRUE
5319,4000,"$25,000-49,999",FALSE
5320,10000,"$25,000-49,999",TRUE
5321,35000,"$100,000+",TRUE
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5324,5000,"$50,000-74,999",FALSE
5325,12000,"$50,000-74,999",TRUE
5326,3500,"$25,000-49,999",FALSE
5327,14000,"$75,000-99,999",FALSE
5328,9000,"$50,000-74,999",FALSE
5329,8000,"$75,000-99,999",TRUE
5330,15000,"$100,000+",TRUE
5331,10000,"$25,000-49,999",TRUE
5332,4000,"$25,000-49,999",FALSE
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5334,1100,"$25,000-49,999",TRUE
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5336,15000,"$50,000-74,999",TRUE
5337,4000,"$25,000-49,999",FALSE
5338,12600,"$100,000+",TRUE
5339,1750,"$1-24,999",FALSE
5340,2809,"$50,000-74,999",TRUE
5341,25000,"$100,000+",FALSE
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5347,7500,"$25,000-49,999",TRUE
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5349,4200,"$50,000-74,999",TRUE
5350,4500,"$25,000-49,999",FALSE
5351,9000,"$25,000-49,999",FALSE
5352,3800,"$25,000-49,999",TRUE
5354,10000,"$25,000-49,999",TRUE
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5357,15000,"$50,000-74,999",TRUE
5358,5000,"$50,000-74,999",TRUE
5359,11500,"$75,000-99,999",TRUE
5360,7000,"$50,000-74,999",TRUE
5361,9000,"$50,000-74,999",TRUE
5362,12000,"$75,000-99,999",TRUE
5363,3000,"$1-24,999",TRUE
5364,15000,"$75,000-99,999",FALSE
5365,4000,"$50,000-74,999",FALSE
5366,15000,"$1-24,999",TRUE
5367,8000,"$75,000-99,999",TRUE
5369,5000,"$50,000-74,999",TRUE
5370,4000,"$25,000-49,999",FALSE
5372,12000,"$50,000-74,999",TRUE
5373,15000,"$100,000+",FALSE
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5377,2000,"$75,000-99,999",FALSE
5378,1000,"$25,000-49,999",TRUE
5380,3000,"$25,000-49,999",TRUE
5381,4500,"$25,000-49,999",FALSE
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5383,6000,"$50,000-74,999",TRUE
5384,10000,"$1-24,999",TRUE
5385,3500,"$100,000+",TRUE
5386,9500,"$75,000-99,999",FALSE
5388,5500,"$25,000-49,999",TRUE
5389,20000,"$50,000-74,999",TRUE
5391,15000,"$50,000-74,999",TRUE
5392,2000,"$50,000-74,999",TRUE
5393,10000,"$75,000-99,999",TRUE
5394,15000,"$50,000-74,999",FALSE
5395,25000,"$75,000-99,999",TRUE
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5397,5566,"$25,000-49,999",TRUE
5398,4000,"$50,000-74,999",FALSE
5399,10000,"$25,000-49,999",TRUE
5400,7500,"$25,000-49,999",FALSE
5402,15000,"$25,000-49,999",FALSE
5403,3000,"$50,000-74,999",TRUE
5404,20000,"$100,000+",TRUE
5405,25000,"$100,000+",TRUE
5406,4000,"$1-24,999",TRUE
5407,10000,"$25,000-49,999",FALSE
5408,2000,"$1-24,999",FALSE
5409,9000,"$50,000-74,999",TRUE
5410,4000,"$50,000-74,999",TRUE
5411,14000,"$50,000-74,999",FALSE
5412,2000,"$25,000-49,999",FALSE
5413,1800,"$25,000-49,999",FALSE
5414,15000,"$100,000+",TRUE
5415,6000,"$1-24,999",FALSE
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5714,9000,"$25,000-49,999",TRUE
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5731,7000,"$25,000-49,999",FALSE
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5763,4000,"$75,000-99,999",TRUE
5764,15000,"$100,000+",TRUE
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5769,3500,"$25,000-49,999",FALSE
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5784,11524,"$25,000-49,999",TRUE
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5794,19000,"$100,000+",TRUE
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5800,8000,"$25,000-49,999",TRUE
5801,3000,"$25,000-49,999",TRUE
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5810,9200,"$50,000-74,999",TRUE
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5813,15000,"$50,000-74,999",TRUE
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5816,15000,"$75,000-99,999",FALSE
5817,9650,"$25,000-49,999",TRUE
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5820,15000,"$75,000-99,999",TRUE
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5823,10000,"$100,000+",TRUE
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5825,17000,"$100,000+",TRUE
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5827,5000,"$50,000-74,999",TRUE
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5829,15000,"$75,000-99,999",TRUE
5830,4000,"$50,000-74,999",TRUE
5831,8750,"$50,000-74,999",FALSE
5832,3921,"$1-24,999",FALSE
5833,15000,"$75,000-99,999",TRUE
5834,4000,"$25,000-49,999",TRUE
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5837,15000,"$100,000+",TRUE
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5840,7500,"$50,000-74,999",FALSE
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5847,12000,"$50,000-74,999",TRUE
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5849,4000,"$75,000-99,999",TRUE
5850,17000,"$75,000-99,999",TRUE
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5867,3000,"$75,000-99,999",TRUE
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5869,15000,"$50,000-74,999",TRUE
5870,1000,"$25,000-49,999",FALSE
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5912,5000,"$75,000-99,999",TRUE
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5914,4000,"$75,000-99,999",TRUE
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5918,1000,"$50,000-74,999",TRUE
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5925,2000,"$50,000-74,999",FALSE
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7108,7000,"$50,000-74,999",FALSE
7109,25000,"$100,000+",TRUE
7110,12000,"$100,000+",TRUE
7111,15000,"$50,000-74,999",TRUE
7112,2000,"$1-24,999",FALSE
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7119,7500,"$25,000-49,999",FALSE
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7154,4100,"$25,000-49,999",TRUE
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7201,11500,"$50,000-74,999",TRUE
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7209,4000,"$50,000-74,999",TRUE
7210,4000,"$25,000-49,999",TRUE
7211,5000,"$25,000-49,999",TRUE
7212,8500,"$50,000-74,999",FALSE
7213,2000,"$25,000-49,999",FALSE
7214,6001,"$25,000-49,999",FALSE
7216,15000,"$75,000-99,999",TRUE
7217,2500,"$100,000+",TRUE
7218,10000,"$50,000-74,999",FALSE
7219,1500,"$25,000-49,999",FALSE
7220,4000,"$25,000-49,999",TRUE
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7225,8500,"$50,000-74,999",FALSE
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7229,15000,"$100,000+",TRUE
7230,15000,"$100,000+",TRUE
7231,4000,"$50,000-74,999",TRUE
7232,6000,"$50,000-74,999",FALSE
7233,7000,"$50,000-74,999",TRUE
7234,15000,"$50,000-74,999",TRUE
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7246,10000,"$25,000-49,999",TRUE
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7249,5000,"$1-24,999",FALSE
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7251,12000,"$50,000-74,999",TRUE
7252,11000,"$50,000-74,999",TRUE
7253,7000,"$75,000-99,999",FALSE
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7256,9000,"$25,000-49,999",FALSE
7257,25000,"$100,000+",TRUE
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7260,5000,"$50,000-74,999",TRUE
7261,4887,"$50,000-74,999",TRUE
7262,10000,"$75,000-99,999",TRUE
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7264,15000,"$50,000-74,999",TRUE
7266,17000,"$75,000-99,999",TRUE
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7280,10000,"$100,000+",FALSE
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7294,8000,"$100,000+",TRUE
7295,15000,"$50,000-74,999",TRUE
7296,15000,"$75,000-99,999",TRUE
7297,5000,"$25,000-49,999",TRUE
7298,10000,"$25,000-49,999",TRUE
7299,15000,"$50,000-74,999",FALSE
7300,4000,"$50,000-74,999",TRUE
7301,15000,"$100,000+",TRUE
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7303,5000,"$25,000-49,999",FALSE
7305,4000,"$100,000+",TRUE
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7307,10000,"$75,000-99,999",TRUE
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7310,4000,"$25,000-49,999",TRUE
7311,9000,"$100,000+",TRUE
7314,25000,"$100,000+",FALSE
7315,2000,"$1-24,999",FALSE
7316,4000,"$25,000-49,999",FALSE
7317,25000,"$100,000+",TRUE
7318,25000,"$100,000+",TRUE
7319,10000,"$100,000+",FALSE
7320,8000,"$75,000-99,999",TRUE
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7322,4000,"$75,000-99,999",TRUE
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7324,7500,"$75,000-99,999",TRUE
7325,17000,"$25,000-49,999",TRUE
7326,3500,"$50,000-74,999",TRUE
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7329,6000,"$1-24,999",FALSE
7330,8500,"$100,000+",TRUE
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7333,9500,"$75,000-99,999",TRUE
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7335,2500,"$25,000-49,999",TRUE
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7338,25000,"$100,000+",FALSE
7339,1000,"$50,000-74,999",FALSE
7340,8000,"$50,000-74,999",TRUE
7341,25000,"$75,000-99,999",TRUE
7342,2500,"$75,000-99,999",FALSE
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7345,15000,"$50,000-74,999",TRUE
7346,10500,"$75,000-99,999",FALSE
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7349,15000,"$50,000-74,999",FALSE
7350,13000,"$75,000-99,999",TRUE
7351,2000,"$25,000-49,999",TRUE
7352,3000,"$75,000-99,999",TRUE
7354,5000,"$25,000-49,999",FALSE
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7356,20000,"$100,000+",TRUE
7357,7900,"$50,000-74,999",TRUE
7358,15000,"$75,000-99,999",TRUE
7359,6500,"$100,000+",TRUE
7360,2500,"$50,000-74,999",TRUE
7361,11000,"$50,000-74,999",FALSE
7362,17000,"$50,000-74,999",TRUE
7363,9000,"$75,000-99,999",TRUE
7364,7000,"$25,000-49,999",TRUE
7365,4500,"$25,000-49,999",FALSE
7366,11000,"$75,000-99,999",FALSE
7367,3000,"$1-24,999",TRUE
7368,10000,"$25,000-49,999",TRUE
7369,3000,"$50,000-74,999",TRUE
7370,15000,"$50,000-74,999",TRUE
7371,1200,"$50,000-74,999",TRUE
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7374,10000,"$75,000-99,999",TRUE
7375,9000,"$50,000-74,999",FALSE
7376,25000,"$75,000-99,999",TRUE
7377,9000,"$75,000-99,999",TRUE
7378,25000,"$100,000+",TRUE
7379,5000,"$50,000-74,999",TRUE
7380,4000,"$1-24,999",FALSE
7381,15000,"$50,000-74,999",TRUE
7382,1000,"$25,000-49,999",FALSE
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7384,17000,"$50,000-74,999",TRUE
7385,15000,"$75,000-99,999",TRUE
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7387,7000,"$75,000-99,999",TRUE
7388,8000,"$100,000+",FALSE
7390,11500,"$100,000+",TRUE
7392,10000,"$75,000-99,999",TRUE
7393,15000,"$50,000-74,999",TRUE
7394,4000,"$50,000-74,999",TRUE
7395,15000,"$50,000-74,999",TRUE
7396,13000,"$50,000-74,999",TRUE
7397,11000,"$25,000-49,999",FALSE
7399,2000,"$25,000-49,999",TRUE
7400,15000,"$25,000-49,999",FALSE
7401,1000,"$25,000-49,999",FALSE
7402,5000,"$25,000-49,999",FALSE
7403,6500,"$50,000-74,999",TRUE
7404,3000,"$100,000+",TRUE
7405,4000,"$25,000-49,999",FALSE
7406,4500,"$25,000-49,999",FALSE
7407,6600,"$75,000-99,999",FALSE
7408,2000,"$50,000-74,999",TRUE
7409,10000,"$25,000-49,999",FALSE
7410,4500,"$50,000-74,999",TRUE
7411,25000,"$100,000+",TRUE
7412,7000,"$50,000-74,999",FALSE
7413,8500,"$50,000-74,999",TRUE
7414,4000,"$25,000-49,999",FALSE
7415,10000,"$75,000-99,999",TRUE
7416,10000,"$50,000-74,999",TRUE
7417,1000,"$25,000-49,999",FALSE
7418,4000,"$75,000-99,999",TRUE
7419,2500,"$1-24,999",FALSE
7420,9500,"$50,000-74,999",TRUE
7421,13000,"$75,000-99,999",FALSE
7422,15000,"$100,000+",TRUE
7423,10000,"$100,000+",TRUE
7424,4000,"$25,000-49,999",FALSE
7425,25000,"$100,000+",FALSE
7426,25000,"$75,000-99,999",TRUE
7427,1300,"$1-24,999",FALSE
7429,4750,"$50,000-74,999",FALSE
7432,8500,"$75,000-99,999",TRUE
7433,15000,"$100,000+",TRUE
7434,10000,"$75,000-99,999",TRUE
7435,1000,"$25,000-49,999",TRUE
7436,4000,"$25,000-49,999",FALSE
7437,4000,"$50,000-74,999",TRUE
7438,3700,"$25,000-49,999",FALSE
7439,12000,"$25,000-49,999",FALSE
7440,16000,"$50,000-74,999",TRUE
7441,4000,"$75,000-99,999",FALSE
7442,1000,"$25,000-49,999",TRUE
7443,2500,"$25,000-49,999",FALSE
7444,15000,"$100,000+",TRUE
7445,15000,"$50,000-74,999",TRUE
7446,12500,"$50,000-74,999",TRUE
7448,4000,"$25,000-49,999",TRUE
7449,2000,"$100,000+",TRUE
7450,10000,"$50,000-74,999",TRUE
7451,3000,"$25,000-49,999",FALSE
7452,8000,"$25,000-49,999",FALSE
7453,15000,"$50,000-74,999",FALSE
7454,3422,"$25,000-49,999",FALSE
7455,9500,"$50,000-74,999",TRUE
7456,15000,"$75,000-99,999",TRUE
7457,20000,"$100,000+",TRUE
7458,20000,"$75,000-99,999",TRUE
7459,2000,"$25,000-49,999",FALSE
7460,2000,"$75,000-99,999",FALSE
7461,15000,"$100,000+",TRUE
7462,14000,"$50,000-74,999",TRUE
7463,4000,"$25,000-49,999",FALSE
7464,13000,"$100,000+",TRUE
7465,15000,"$50,000-74,999",FALSE
7466,10000,"$50,000-74,999",FALSE
7467,22500,"$50,000-74,999",TRUE
7468,7500,"$50,000-74,999",FALSE
7469,3500,"$50,000-74,999",TRUE
7470,3300,"$25,000-49,999",FALSE
7471,7000,"$25,000-49,999",FALSE
7472,5000,"$50,000-74,999",FALSE
7473,6000,"$25,000-49,999",TRUE
7475,16500,"$100,000+",TRUE
7476,20000,"$75,000-99,999",TRUE
7477,10000,"$50,000-74,999",TRUE
7479,4000,"$75,000-99,999",FALSE
7481,5500,"$25,000-49,999",FALSE
7482,14500,$0,FALSE
7483,25000,"$50,000-74,999",FALSE
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8311,3600,"$25,000-49,999",TRUE
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8313,5000,"$25,000-49,999",TRUE
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8377,2100,"$100,000+",TRUE
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8379,15000,"$50,000-74,999",TRUE
8380,2000,"$75,000-99,999",TRUE
8381,4000,"$25,000-49,999",TRUE
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8384,15000,"$50,000-74,999",TRUE
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8391,25000,"$75,000-99,999",TRUE
8392,2000,"$100,000+",TRUE
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8395,6500,"$50,000-74,999",TRUE
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8400,4000,"$100,000+",TRUE
8401,18000,"$100,000+",TRUE
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8405,15000,"$50,000-74,999",TRUE
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8410,15000,"$50,000-74,999",FALSE
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8414,2750,"$50,000-74,999",FALSE
8415,4000,"$25,000-49,999",FALSE
8416,13800,"$100,000+",TRUE
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8419,9000,"$25,000-49,999",TRUE
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8423,7500,"$25,000-49,999",TRUE
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8431,9000,"$25,000-49,999",FALSE
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8438,14000,"$50,000-74,999",TRUE
8439,25000,"$50,000-74,999",TRUE
8440,6000,"$100,000+",FALSE
8442,5500,$0,TRUE
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8469,15000,"$50,000-74,999",TRUE
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8498,15000,"$100,000+",TRUE
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8501,3000,"$25,000-49,999",FALSE
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8506,4000,"$25,000-49,999",TRUE
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8508,15000,"$25,000-49,999",TRUE
8509,1000,"$25,000-49,999",FALSE
8510,4000,"$25,000-49,999",TRUE
8511,4000,"$100,000+",TRUE
8512,8000,"$25,000-49,999",FALSE
8514,7500,"$1-24,999",FALSE
8515,14000,"$50,000-74,999",TRUE
8516,3658,"$25,000-49,999",FALSE
8517,3000,"$25,000-49,999",FALSE
8518,9422,"$75,000-99,999",TRUE
8519,5050,"$1-24,999",FALSE
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8521,4000,"$25,000-49,999",FALSE
8522,15000,"$50,000-74,999",FALSE
8523,1000,"$25,000-49,999",FALSE
8524,4000,"$1-24,999",TRUE
8525,4000,"$25,000-49,999",FALSE
8526,4000,"$1-24,999",TRUE
8528,1500,"$25,000-49,999",TRUE
8529,20000,"$100,000+",FALSE
8530,3000,"$1-24,999",TRUE
8531,10000,"$25,000-49,999",FALSE
8532,25000,"$75,000-99,999",TRUE
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12360,5000,"$25,000-49,999",TRUE
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12408,7500,"$75,000-99,999",TRUE
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12412,10000,"$1-24,999",FALSE
12413,7500,"$25,000-49,999",TRUE
12415,9000,"$25,000-49,999",TRUE
12416,20000,"$75,000-99,999",TRUE
12417,2500,"$50,000-74,999",TRUE
12419,3000,"$50,000-74,999",TRUE
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12421,4000,"$50,000-74,999",TRUE
12422,11500,"$50,000-74,999",TRUE
12423,15000,"$75,000-99,999",TRUE
12424,35000,"$100,000+",TRUE
12425,8000,"$25,000-49,999",FALSE
12426,12000,"$50,000-74,999",TRUE
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12428,4000,"$50,000-74,999",TRUE
12429,3000,"$100,000+",TRUE
12430,12000,"$50,000-74,999",TRUE
12431,3001,"$25,000-49,999",TRUE
12432,1000,"$25,000-49,999",FALSE
12434,3000,"$1-24,999",TRUE
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12436,12000,"$25,000-49,999",TRUE
12438,15000,"$25,000-49,999",FALSE
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12440,15000,"$25,000-49,999",TRUE
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12458,4000,"$25,000-49,999",TRUE
12459,25000,"$75,000-99,999",TRUE
12460,12000,"$75,000-99,999",TRUE
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12463,4000,"$50,000-74,999",TRUE
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12470,9500,"$50,000-74,999",FALSE
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12475,9000,$0,TRUE
12477,10000,"$75,000-99,999",TRUE
12478,4000,"$50,000-74,999",FALSE
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12498,10000,"$75,000-99,999",TRUE
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12505,20000,"$75,000-99,999",TRUE
12506,20000,"$100,000+",TRUE
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12508,4000,"$25,000-49,999",TRUE
12509,7000,"$100,000+",TRUE
12510,15000,"$50,000-74,999",FALSE
12511,25000,"$25,000-49,999",TRUE
12512,7000,$0,TRUE
12513,8500,"$25,000-49,999",TRUE
12514,12731,"$100,000+",TRUE
12515,15000,"$100,000+",TRUE
12516,3000,"$50,000-74,999",FALSE
12517,7500,"$50,000-74,999",TRUE
12518,1000,"$25,000-49,999",FALSE
12519,3950,"$25,000-49,999",FALSE
12520,3000,"$25,000-49,999",TRUE
12521,25000,"$100,000+",TRUE
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12525,4000,"$25,000-49,999",FALSE
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12532,13000,"$50,000-74,999",TRUE
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12537,5000,"$100,000+",TRUE
12538,10000,"$25,000-49,999",FALSE
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12549,5000,"$75,000-99,999",TRUE
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12551,8000,"$75,000-99,999",TRUE
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12569,10000,"$25,000-49,999",TRUE
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12571,15000,"$50,000-74,999",TRUE
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12575,7500,"$75,000-99,999",TRUE
12576,3000,"$1-24,999",TRUE
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12579,4000,"$50,000-74,999",TRUE
12580,3500,"$100,000+",TRUE
12581,12000,"$50,000-74,999",FALSE
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12583,10000,"$50,000-74,999",TRUE
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12587,3000,"$100,000+",TRUE
12589,2500,"$50,000-74,999",FALSE
12590,15000,"$50,000-74,999",TRUE
12591,3000,"$25,000-49,999",TRUE
12592,15000,"$50,000-74,999",FALSE
12593,10000,"$25,000-49,999",TRUE
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12598,7500,"$50,000-74,999",TRUE
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12601,8000,"$100,000+",TRUE
12602,15000,"$100,000+",TRUE
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12605,13000,"$50,000-74,999",TRUE
12606,15000,"$50,000-74,999",TRUE
12607,4000,"$25,000-49,999",FALSE
12608,1000,"$75,000-99,999",FALSE
12609,15036,"$1-24,999",TRUE
12610,6000,"$25,000-49,999",TRUE
12611,18000,"$75,000-99,999",TRUE
12612,2300,"$50,000-74,999",FALSE
12613,6000,"$100,000+",TRUE
12614,15000,"$25,000-49,999",TRUE
12615,4000,"$25,000-49,999",FALSE
12616,25000,"$75,000-99,999",TRUE
12617,25000,"$75,000-99,999",TRUE
12618,15000,"$50,000-74,999",FALSE
12619,25000,"$100,000+",TRUE
12620,15000,"$75,000-99,999",TRUE
12621,2000,"$100,000+",TRUE
12622,4000,"$25,000-49,999",TRUE
12623,25000,"$100,000+",TRUE
12624,15000,"$50,000-74,999",FALSE
12625,11000,"$50,000-74,999",FALSE
12627,7500,"$100,000+",FALSE
12628,10000,"$75,000-99,999",TRUE
12630,2500,"$50,000-74,999",FALSE
12631,9000,"$25,000-49,999",TRUE
12632,7500,"$25,000-49,999",FALSE
12633,4000,"$50,000-74,999",TRUE
12634,2000,"$50,000-74,999",TRUE
12635,15000,"$100,000+",FALSE
12636,10000,"$75,000-99,999",TRUE
12637,5000,"$75,000-99,999",TRUE
12638,3200,"$1-24,999",FALSE
12640,6000,"$50,000-74,999",TRUE
12643,4500,"$50,000-74,999",FALSE
12644,4000,"$25,000-49,999",TRUE
12645,12000,"$100,000+",FALSE
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12649,15000,"$50,000-74,999",TRUE
12650,2000,"$50,000-74,999",FALSE
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12657,10000,"$75,000-99,999",TRUE
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12660,20000,"$75,000-99,999",TRUE
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12663,9500,"$50,000-74,999",TRUE
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12665,4000,"$25,000-49,999",FALSE
12666,25000,"$100,000+",TRUE
12667,7000,"$25,000-49,999",TRUE
12668,1200,"$50,000-74,999",TRUE
12669,6000,"$75,000-99,999",TRUE
12670,4000,"$50,000-74,999",FALSE
12671,3000,"$100,000+",FALSE
12672,9000,"$75,000-99,999",FALSE
12674,1400,"$1-24,999",FALSE
12675,10000,"$25,000-49,999",TRUE
12676,3000,"$50,000-74,999",TRUE
12677,10000,"$100,000+",TRUE
12678,3000,"$25,000-49,999",FALSE
12679,4000,"$25,000-49,999",TRUE
12680,10000,"$75,000-99,999",TRUE
12681,10000,"$100,000+",TRUE
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12684,6500,"$75,000-99,999",FALSE
12685,4000,"$25,000-49,999",FALSE
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12689,35000,"$100,000+",FALSE
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12691,25000,"$25,000-49,999",TRUE
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12695,25000,"$75,000-99,999",TRUE
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12699,4000,"$50,000-74,999",TRUE
12700,4000,"$75,000-99,999",FALSE
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12703,15000,"$100,000+",TRUE
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12705,4000,"$75,000-99,999",TRUE
12706,1250,"$25,000-49,999",FALSE
12707,7000,"$50,000-74,999",FALSE
12708,2500,"$50,000-74,999",TRUE
12709,3000,"$75,000-99,999",TRUE
12710,25000,"$100,000+",TRUE
12711,24000,"$100,000+",TRUE
12712,6001,"$50,000-74,999",FALSE
12713,4000,"$50,000-74,999",FALSE
12714,11000,"$25,000-49,999",FALSE
12715,6500,"$75,000-99,999",TRUE
12716,3500,"$25,000-49,999",FALSE
12717,19000,"$100,000+",TRUE
12719,15000,"$100,000+",TRUE
12720,2000,"$1-24,999",FALSE
12721,2000,"$75,000-99,999",TRUE
12722,4000,"$50,000-74,999",FALSE
12723,5000,"$75,000-99,999",FALSE
12724,5000,"$50,000-74,999",TRUE
12725,7000,"$75,000-99,999",TRUE
12726,5000,"$75,000-99,999",FALSE
12727,25000,"$100,000+",TRUE
12728,3000,"$50,000-74,999",TRUE
12729,1000,"$1-24,999",FALSE
12730,2551,"$75,000-99,999",FALSE
12731,3000,"$50,000-74,999",FALSE
12732,4500,"$100,000+",TRUE
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13639,7000,"$75,000-99,999",TRUE
13640,9000,"$50,000-74,999",TRUE
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13713,2000,"$25,000-49,999",FALSE
13715,15000,"$25,000-49,999",TRUE
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13718,10000,"$50,000-74,999",TRUE
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13776,18000,"$100,000+",TRUE
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15319,10000,"$50,000-74,999",TRUE
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15363,5000,"$50,000-74,999",TRUE
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15713,7500,"$50,000-74,999",TRUE
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15812,4000,"$100,000+",TRUE
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15814,25000,"$75,000-99,999",TRUE
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15825,15000,"$25,000-49,999",TRUE
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15836,9000,"$1-24,999",TRUE
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15839,4500,"$25,000-49,999",TRUE
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16711,15000,"$75,000-99,999",TRUE
16712,7429,"$25,000-49,999",TRUE
16713,20000,"$100,000+",TRUE
16714,25000,"$100,000+",TRUE
16715,2000,"$25,000-49,999",TRUE
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16737,4000,"$25,000-49,999",TRUE
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16801,5000,"$25,000-49,999",FALSE
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16809,9000,"$100,000+",TRUE
16810,15000,"$100,000+",TRUE
16811,4000,"$25,000-49,999",FALSE
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16814,10000,"$50,000-74,999",TRUE
16815,15000,"$100,000+",TRUE
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16818,25000,"$75,000-99,999",TRUE
16819,4800,"$100,000+",TRUE
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16827,25000,"$100,000+",TRUE
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16836,20000,"$75,000-99,999",TRUE
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16839,15000,"$75,000-99,999",FALSE
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16845,15000,"$100,000+",TRUE
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16848,4500,"$25,000-49,999",FALSE
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16850,10000,"$1-24,999",FALSE
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16875,15000,"$75,000-99,999",TRUE
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16904,10000,"$25,000-49,999",TRUE
16905,8055,"$25,000-49,999",TRUE
16906,7000,"$25,000-49,999",TRUE
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16912,6001,"$75,000-99,999",TRUE
16913,6000,"$25,000-49,999",FALSE
16914,15000,"$100,000+",TRUE
16915,7500,"$75,000-99,999",TRUE
16918,4000,"$50,000-74,999",TRUE
16920,10000,"$25,000-49,999",FALSE
16922,11000,"$25,000-49,999",TRUE
16923,10000,"$50,000-74,999",FALSE
16924,3000,"$1-24,999",FALSE
16925,9000,"$50,000-74,999",TRUE
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16929,2500,"$75,000-99,999",TRUE
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16932,5000,"$50,000-74,999",TRUE
16933,15000,"$25,000-49,999",FALSE
16934,7000,"$100,000+",FALSE
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17713,5000,"$50,000-74,999",TRUE
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17715,10000,"$50,000-74,999",TRUE
17716,15000,"$100,000+",TRUE
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17811,4500,"$100,000+",TRUE
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17814,15000,"$50,000-74,999",TRUE
17815,2800,"$100,000+",TRUE
17816,15000,"$50,000-74,999",TRUE
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17838,35000,"$100,000+",TRUE
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17860,4000,"$50,000-74,999",TRUE
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17864,25000,"$100,000+",TRUE
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17887,25000,"$75,000-99,999",TRUE
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17905,3000,"$25,000-49,999",TRUE
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17910,15000,"$75,000-99,999",FALSE
17911,10000,"$100,000+",TRUE
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17914,15000,"$50,000-74,999",TRUE
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17917,13000,"$50,000-74,999",FALSE
17918,12000,"$50,000-74,999",TRUE
17919,12000,"$25,000-49,999",FALSE
17921,35000,"$100,000+",TRUE
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17923,5325,"$25,000-49,999",FALSE
17924,3500,"$25,000-49,999",TRUE
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17932,3000,"$25,000-49,999",TRUE
17933,8500,"$25,000-49,999",TRUE
17934,25000,"$100,000+",FALSE
17935,15000,"$50,000-74,999",TRUE
17936,15000,"$25,000-49,999",TRUE
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17960,8000,"$50,000-74,999",TRUE
17961,4000,"$50,000-74,999",TRUE
17962,5000,"$75,000-99,999",TRUE
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17965,2000,"$25,000-49,999",FALSE
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17967,2000,"$1-24,999",FALSE
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19545,15000,"$25,000-49,999",TRUE
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19550,5000,"$50,000-74,999",TRUE
19551,10000,"$75,000-99,999",TRUE
19553,15000,"$50,000-74,999",TRUE
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19561,25000,"$75,000-99,999",TRUE
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19563,15000,"$50,000-74,999",TRUE
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19571,10000,"$100,000+",TRUE
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19575,20000,"$100,000+",TRUE
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19578,4500,"$100,000+",TRUE
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19589,4000,"$75,000-99,999",TRUE
19590,15206,"$100,000+",TRUE
19591,15000,"$50,000-74,999",TRUE
19592,15000,"$100,000+",TRUE
19593,7000,"$50,000-74,999",TRUE
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19598,11500,"$50,000-74,999",TRUE
19599,7000,"$50,000-74,999",TRUE
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19601,13000,"$25,000-49,999",TRUE
19602,13000,"$100,000+",TRUE
19603,15000,"$75,000-99,999",TRUE
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19605,4000,"$25,000-49,999",FALSE
19606,15000,"$100,000+",TRUE
19607,9000,"$50,000-74,999",TRUE
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19610,17000,"$50,000-74,999",FALSE
19611,4200,"$25,000-49,999",FALSE
19612,17873,"$50,000-74,999",FALSE
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19615,4500,"$25,000-49,999",FALSE
19616,5000,"$75,000-99,999",FALSE
19617,15000,"$100,000+",TRUE
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19620,7000,"$75,000-99,999",TRUE
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19634,28000,"$100,000+",TRUE
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19636,10000,"$50,000-74,999",TRUE
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19639,10000,"$25,000-49,999",FALSE
19640,5500,"$75,000-99,999",TRUE
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19642,7000,"$25,000-49,999",TRUE
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19646,20000,"$75,000-99,999",TRUE
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19650,10000,"$25,000-49,999",TRUE
19651,13500,"$50,000-74,999",FALSE
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19654,10000,"$100,000+",TRUE
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19658,6200,"$25,000-49,999",FALSE
19659,4850,"$75,000-99,999",TRUE
19660,2000,"$50,000-74,999",TRUE
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19662,12000,"$100,000+",TRUE
19663,1400,"$50,000-74,999",TRUE
19664,13000,"$100,000+",FALSE
19665,20000,"$100,000+",TRUE
19666,25000,"$75,000-99,999",TRUE
19667,15000,"$50,000-74,999",FALSE
19668,8500,"$1-24,999",FALSE
19669,9000,"$50,000-74,999",TRUE
19670,4000,"$50,000-74,999",FALSE
19671,10000,"$25,000-49,999",FALSE
19672,8000,"$100,000+",TRUE
19674,6000,"$75,000-99,999",TRUE
19675,10000,"$100,000+",TRUE
19676,8000,"$75,000-99,999",TRUE
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19678,2000,"$50,000-74,999",TRUE
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19680,1000,"$50,000-74,999",TRUE
19681,5000,"$50,000-74,999",TRUE
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19683,7500,"$75,000-99,999",FALSE
19684,15000,"$25,000-49,999",TRUE
19685,3500,"$25,000-49,999",TRUE
19686,15000,"$50,000-74,999",TRUE
19687,3000,"$25,000-49,999",FALSE
19688,8000,"$75,000-99,999",FALSE
19689,3250,"$75,000-99,999",TRUE
19690,13000,"$50,000-74,999",TRUE
19691,4500,"$100,000+",TRUE
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19693,12500,"$100,000+",TRUE
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19698,3000,"$100,000+",TRUE
19699,1000,"$25,000-49,999",FALSE
19700,10000,"$50,000-74,999",TRUE
19701,20000,"$50,000-74,999",FALSE
19703,1000,"$100,000+",TRUE
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19705,5000,"$25,000-49,999",FALSE
19706,10001,"$25,000-49,999",TRUE
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19708,10102,"$75,000-99,999",TRUE
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19711,5000,"$50,000-74,999",TRUE
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19714,19000,"$100,000+",FALSE
19715,10000,"$50,000-74,999",TRUE
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19721,8500,"$25,000-49,999",FALSE
19722,20000,"$100,000+",TRUE
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19724,4000,"$100,000+",TRUE
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19726,10000,"$50,000-74,999",TRUE
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19729,15000,"$75,000-99,999",TRUE
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19735,25000,"$75,000-99,999",TRUE
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19750,7000,"$100,000+",TRUE
19751,10000,"$50,000-74,999",TRUE
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19753,10000,"$50,000-74,999",TRUE
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19757,15000,"$75,000-99,999",TRUE
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19760,10500,"$25,000-49,999",TRUE
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19773,10000,"$25,000-49,999",TRUE
19775,15000,"$100,000+",TRUE
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19780,10000,"$50,000-74,999",TRUE
19782,6000,"$50,000-74,999",TRUE
19783,6000,"$50,000-74,999",TRUE
19784,8000,"$75,000-99,999",TRUE
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19787,15000,"$100,000+",FALSE
19788,2000,"$50,000-74,999",TRUE
19789,3000,"$25,000-49,999",FALSE
19790,2000,"$25,000-49,999",FALSE
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19793,2000,"$25,000-49,999",FALSE
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19797,2500,"$25,000-49,999",TRUE
19800,3100,"$50,000-74,999",TRUE
19801,6001,"$75,000-99,999",FALSE
19802,4000,"$100,000+",TRUE
19803,6500,"$50,000-74,999",TRUE
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19811,9000,"$50,000-74,999",TRUE
19812,4949,"$75,000-99,999",TRUE
19813,10000,"$25,000-49,999",TRUE
19814,1600,"$50,000-74,999",TRUE
19815,15500,"$50,000-74,999",TRUE
19816,15000,"$50,000-74,999",FALSE
19817,20000,"$100,000+",TRUE
19818,4000,"$75,000-99,999",TRUE
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19821,12144,"$50,000-74,999",FALSE
19822,13000,"$50,000-74,999",TRUE
19823,20000,"$50,000-74,999",TRUE
19824,18000,"$100,000+",TRUE
19825,13750,"$50,000-74,999",TRUE
19826,5000,"$1-24,999",FALSE
19827,10000,"$25,000-49,999",TRUE
19828,15000,"$50,000-74,999",FALSE
19829,4000,"$25,000-49,999",TRUE
19830,5449,"$50,000-74,999",TRUE
19831,2000,"$50,000-74,999",TRUE
19832,6500,"$100,000+",TRUE
19833,2000,"$25,000-49,999",FALSE
19834,10000,"$100,000+",FALSE
19835,5000,"$25,000-49,999",FALSE
19836,4000,"$25,000-49,999",FALSE
19837,16000,$0,TRUE
19838,13100,"$100,000+",TRUE
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19840,4000,"$100,000+",TRUE
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19844,7500,"$75,000-99,999",FALSE
19845,20000,"$25,000-49,999",TRUE
19846,15000,"$100,000+",TRUE
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19848,11000,"$25,000-49,999",TRUE
19849,4000,"$50,000-74,999",TRUE
19850,5000,"$25,000-49,999",TRUE
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19853,4000,"$50,000-74,999",FALSE
19854,18000,"$100,000+",TRUE
19855,10000,"$75,000-99,999",TRUE
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19860,7000,"$50,000-74,999",TRUE
19861,10000,"$25,000-49,999",TRUE
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19863,7000,"$100,000+",TRUE
19864,15000,"$100,000+",TRUE
19865,4000,"$75,000-99,999",TRUE
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19870,10000,"$75,000-99,999",TRUE
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19873,3000,"$25,000-49,999",TRUE
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19877,10000,"$1-24,999",TRUE
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19890,10000,"$25,000-49,999",TRUE
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19893,4500,"$50,000-74,999",TRUE
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19912,3000,"$25,000-49,999",TRUE
19913,13500,"$50,000-74,999",TRUE
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19971,9000,"$25,000-49,999",TRUE
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19973,7000,"$50,000-74,999",FALSE
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20050,7000,"$75,000-99,999",TRUE
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20197,10000,"$50,000-74,999",TRUE
20198,6892,"$50,000-74,999",TRUE
20199,3000,"$1-24,999",TRUE
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20201,6000,"$25,000-49,999",TRUE
20202,5700,"$75,000-99,999",TRUE
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20204,15000,"$25,000-49,999",FALSE
20205,5000,"$50,000-74,999",TRUE
20207,4000,"$25,000-49,999",TRUE
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20217,15000,"$50,000-74,999",TRUE
20218,10000,"$25,000-49,999",TRUE
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20310,15000,"$75,000-99,999",TRUE
20311,20000,"$100,000+",TRUE
20312,25000,"$75,000-99,999",TRUE
20313,6958,"$100,000+",FALSE
20315,2000,"$25,000-49,999",FALSE
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20317,11000,"$50,000-74,999",TRUE
20318,18000,"$75,000-99,999",TRUE
20320,5000,"$25,000-49,999",FALSE
20321,25000,"$100,000+",TRUE
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20323,5000,"$25,000-49,999",TRUE
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20325,25000,"$75,000-99,999",TRUE
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20327,7500,"$25,000-49,999",TRUE
20328,7000,"$25,000-49,999",TRUE
20329,6000,"$25,000-49,999",TRUE
20330,20000,"$100,000+",TRUE
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20364,2000,"$50,000-74,999",TRUE
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20398,7000,"$75,000-99,999",TRUE
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20460,8500,"$100,000+",TRUE
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20465,20000,"$100,000+",TRUE
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20501,4000,"$75,000-99,999",TRUE
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20513,10000,"$50,000-74,999",TRUE
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20521,10000,"$50,000-74,999",TRUE
20522,1500,"$25,000-49,999",TRUE
20523,10000,"$50,000-74,999",TRUE
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20575,30000,"$100,000+",TRUE
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21969,15000,"$50,000-74,999",TRUE
21970,25000,"$100,000+",TRUE
21971,10000,"$50,000-74,999",TRUE
21972,15000,"$50,000-74,999",FALSE
21973,2551,"$1-24,999",FALSE
21974,20000,"$50,000-74,999",TRUE
21975,17500,"$25,000-49,999",FALSE
21976,14450,"$50,000-74,999",FALSE
21978,10500,"$1-24,999",FALSE
21979,15000,"$50,000-74,999",TRUE
21980,20000,"$100,000+",FALSE
21981,6000,"$50,000-74,999",FALSE
21982,5000,"$75,000-99,999",TRUE
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21984,15000,"$100,000+",TRUE
21985,8500,"$50,000-74,999",TRUE
21986,3000,"$25,000-49,999",FALSE
21987,15000,"$25,000-49,999",FALSE
21988,2500,"$25,000-49,999",FALSE
21989,4000,"$25,000-49,999",TRUE
21990,2000,"$25,000-49,999",FALSE
21991,21500,"$100,000+",FALSE
21992,4300,"$25,000-49,999",FALSE
21993,3000,"$75,000-99,999",FALSE
21994,15000,"$50,000-74,999",FALSE
21995,13000,"$25,000-49,999",TRUE
21996,5950,"$50,000-74,999",FALSE
21997,4800,"$25,000-49,999",FALSE
21998,6300,"$50,000-74,999",FALSE
21999,10000,"$25,000-49,999",FALSE
22001,9000,"$50,000-74,999",FALSE
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22003,12500,"$50,000-74,999",TRUE
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22006,11000,"$75,000-99,999",TRUE
22007,2000,"$50,000-74,999",TRUE
22009,4000,"$100,000+",TRUE
22010,15000,"$100,000+",FALSE
22011,10000,"$25,000-49,999",TRUE
22012,4587,"$25,000-49,999",FALSE
22013,12000,"$50,000-74,999",TRUE
22014,4000,"$75,000-99,999",TRUE
22015,4000,"$75,000-99,999",TRUE
22016,4499,"$25,000-49,999",TRUE
22017,16000,"$75,000-99,999",TRUE
22018,2000,"$1-24,999",FALSE
22019,13500,"$25,000-49,999",FALSE
22020,2500,"$50,000-74,999",FALSE
22021,5000,"$100,000+",TRUE
22022,4500,"$50,000-74,999",FALSE
22023,5000,"$75,000-99,999",TRUE
22024,2500,"$50,000-74,999",TRUE
22025,6000,"$1-24,999",FALSE
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22030,15000,"$50,000-74,999",TRUE
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22033,5000,"$100,000+",TRUE
22034,4000,"$1-24,999",TRUE
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22086,5000,"$50,000-74,999",TRUE
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22088,6475,"$1-24,999",TRUE
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23440,4000,"$75,000-99,999",TRUE
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23523,15000,"$50,000-74,999",TRUE
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23532,10000,"$75,000-99,999",TRUE
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23538,3500,"$50,000-74,999",TRUE
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23560,5000,"$25,000-49,999",TRUE
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23564,10500,"$75,000-99,999",TRUE
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23589,9000,"$25,000-49,999",TRUE
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23606,5000,$0,TRUE
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23611,4000,"$25,000-49,999",TRUE
23612,3500,"$25,000-49,999",FALSE
23613,3000,"$25,000-49,999",TRUE
23614,5000,"$100,000+",TRUE
23615,10000,"$75,000-99,999",TRUE
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23617,2277,"$25,000-49,999",TRUE
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23623,3500,"$100,000+",TRUE
23625,11000,"$50,000-74,999",FALSE
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23627,3500,"$50,000-74,999",TRUE
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23631,6000,"$75,000-99,999",TRUE
23632,13000,"$50,000-74,999",TRUE
23634,11500,"$100,000+",TRUE
23635,4000,"$50,000-74,999",TRUE
23636,10000,"$25,000-49,999",FALSE
23638,3000,"$25,000-49,999",TRUE
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23640,15000,"$50,000-74,999",TRUE
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23650,35000,"$100,000+",TRUE
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23658,25000,"$100,000+",TRUE
23659,1250,"$75,000-99,999",TRUE
23660,3000,"$50,000-74,999",TRUE
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23685,15000,"$25,000-49,999",TRUE
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23704,15000,"$50,000-74,999",TRUE
23706,2600,"$25,000-49,999",FALSE
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23708,2000,"$1-24,999",FALSE
23709,20000,"$75,000-99,999",TRUE
23710,3000,"$25,000-49,999",TRUE
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24513,3000,"$100,000+",TRUE
24514,15000,"$50,000-74,999",FALSE
24515,25000,"$1-24,999",FALSE
24516,17051,"$50,000-74,999",TRUE
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24518,18000,"$100,000+",TRUE
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24566,14000,"$100,000+",TRUE
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24599,15000,"$50,000-74,999",TRUE
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24614,13863,"$25,000-49,999",FALSE
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24640,10000,"$25,000-49,999",TRUE
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24669,7500,"$75,000-99,999",TRUE
24670,15000,"$100,000+",TRUE
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24699,3500,"$1-24,999",TRUE
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24701,4000,"$25,000-49,999",TRUE
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24707,15000,"$100,000+",TRUE
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24710,4000,"$25,000-49,999",FALSE
24711,20000,"$100,000+",TRUE
24712,22500,$0,TRUE
24713,2000,"$1-24,999",FALSE
24714,15000,"$75,000-99,999",FALSE
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24717,20000,"$50,000-74,999",TRUE
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24720,25000,"$50,000-74,999",TRUE
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24729,2100,"$1-24,999",TRUE
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24759,10000,"$25,000-49,999",TRUE
24760,6000,"$25,000-49,999",TRUE
24761,15000,"$75,000-99,999",FALSE
24762,4000,"$50,000-74,999",TRUE
24763,10000,"$100,000+",FALSE
24764,10500,"$50,000-74,999",FALSE
24765,4000,"$50,000-74,999",TRUE
24766,15000,"$100,000+",TRUE
24767,20000,"$100,000+",FALSE
24768,4000,"$25,000-49,999",TRUE
24769,9000,"$1-24,999",TRUE
24770,10500,"$50,000-74,999",FALSE
24771,9039,"$50,000-74,999",FALSE
24772,15000,"$50,000-74,999",TRUE
24773,1477,"$25,000-49,999",TRUE
24774,20000,"$75,000-99,999",TRUE
24775,2000,"$50,000-74,999",TRUE
24776,4000,"$50,000-74,999",TRUE
24777,22500,"$50,000-74,999",TRUE
24778,25000,"$100,000+",TRUE
24779,4000,"$25,000-49,999",FALSE
24780,2600,"$25,000-49,999",TRUE
24781,5000,"$50,000-74,999",TRUE
24782,25000,"$100,000+",FALSE
24783,15000,"$50,000-74,999",TRUE
24784,17000,"$50,000-74,999",FALSE
24785,2000,"$50,000-74,999",FALSE
24786,15000,"$50,000-74,999",TRUE
24787,3690,"$50,000-74,999",FALSE
24788,10000,"$25,000-49,999",TRUE
24789,3000,"$25,000-49,999",FALSE
24791,15000,"$50,000-74,999",FALSE
24793,33000,"$100,000+",TRUE
24794,7000,"$50,000-74,999",FALSE
24795,2000,"$25,000-49,999",FALSE
24796,10000,"$75,000-99,999",FALSE
24797,12500,"$50,000-74,999",FALSE
24798,4000,"$50,000-74,999",FALSE
24799,25000,"$50,000-74,999",FALSE
24800,10000,"$100,000+",FALSE
24801,8000,"$25,000-49,999",TRUE
24802,15000,"$100,000+",TRUE
24803,1000,"$25,000-49,999",FALSE
24804,10500,"$50,000-74,999",TRUE
24805,25000,"$100,000+",FALSE
24806,15000,"$50,000-74,999",FALSE
24807,3000,"$50,000-74,999",TRUE
24808,15000,"$75,000-99,999",TRUE
24809,3500,"$50,000-74,999",FALSE
24810,25000,"$75,000-99,999",FALSE
24811,12000,"$50,000-74,999",TRUE
24814,15000,"$75,000-99,999",FALSE
24815,2000,"$25,000-49,999",FALSE
24817,2050,"$50,000-74,999",FALSE
24818,13750,"$50,000-74,999",TRUE
24819,7000,"$75,000-99,999",TRUE
24821,15000,"$50,000-74,999",TRUE
24822,3500,"$75,000-99,999",TRUE
24823,5000,"$25,000-49,999",FALSE
24824,2000,"$50,000-74,999",FALSE
24825,5000,"$75,000-99,999",FALSE
24826,12000,"$75,000-99,999",TRUE
24827,2500,"$75,000-99,999",FALSE
24828,4000,"$75,000-99,999",TRUE
24829,2000,"$75,000-99,999",FALSE
24830,8000,"$50,000-74,999",FALSE
24831,10000,"$50,000-74,999",FALSE
24832,5000,"$50,000-74,999",TRUE
24833,13000,"$75,000-99,999",TRUE
24834,10000,"$50,000-74,999",FALSE
24836,3000,"$1-24,999",FALSE
24837,4000,"$75,000-99,999",TRUE
24838,10500,"$50,000-74,999",FALSE
24840,10000,"$50,000-74,999",FALSE
24841,6500,"$100,000+",FALSE
24842,5400,"$75,000-99,999",FALSE
24843,4000,"$100,000+",TRUE
24844,5000,"$25,000-49,999",FALSE
24845,3800,"$100,000+",TRUE
24847,25000,"$75,000-99,999",TRUE
24848,15000,"$75,000-99,999",TRUE
24849,4000,"$1-24,999",FALSE
24850,3550,"$1-24,999",FALSE
24851,13000,"$100,000+",TRUE
24852,9600,"$50,000-74,999",TRUE
24853,7000,"$100,000+",TRUE
24854,3500,"$50,000-74,999",TRUE
24856,12000,"$75,000-99,999",TRUE
24857,5400,"$50,000-74,999",