Using historical housing price data for an area, determine the minimum appreciation for every year interval

historical_housing_prices_analyzer.rb

prices = [
  247901,
  313230,
  362438,
  361772,
  345520,
  338346,
  329087,
  328406,
  342284,
  365954,
  393909,
  479031,
  560485,
  728901,
  751361,
  751273,
  810006,
  947624,
  1079112,
  1103005,
  1204167,
  1116976,
  961689,
  996505,
  948797,
  1103974,
  1291523,
  1444416,
  1631313,
  1709147
]

def median(ary)
  mid = ary.length / 2
  sorted = ary.sort
  ary.length.odd? ? sorted[mid] : 0.5 * (sorted[mid] + sorted[mid - 1])
end


c = prices.count
puts "prices: #{c}"
nets = {}
c.times do |i|
  c.times do |years|
    next if years == 0
    next if i+years > c
    slice = prices[i.. i+years]
    # puts slice.join(',')
    net = slice.last - slice.first
    nets[years] ||= []
    nets[years] << net
  end
end


puts %w(years avg median min max count years).join("\t")
nets.each do |years, arr|
  sum = arr.inject{ |acc, el| acc + el }.to_i
  avg = sum / arr.count
  puts [years, avg, median(arr).to_i, arr.min, arr.max, arr.count, years].join(?\t)
end

# prices: 30
# years	avg	median	min	max	count	years
# 1	48708	31385	-155287	187549	30	1
# 2	98522	77834	-242478	342726	29	2
# 3	147358	139516	-207662	527339	28	3
# 4	192914	218201	-255370	682516	27	4
# 5	236718	269524	-154208	760350	26	5
# 6	276812	327440	-130315	760350	25	6
# 7	314167	302932	1173	747458	24	7
# 8	356874	388075	31471	747458	23	8
# 9	401251	398198	80679	725136	22	9
# 10	456121	480919	146008	810258	21	10
# 11	519440	511454	219896	838213	20	11
# 12	579620	567780	312584	861883	19	12
# 13	640633	602350	388835	899141	18	13
# 14	691184	715515	438043	957874	17	14
# 15	738048	737412	496776	957874	16	15
# 16	787251	741233	562105	1070828	15	16
# 17	839295	768669	620391	1152282	14	17
# 18	887031	831211	616169	1237404	13	18
# 19	917731	873020	599917	1315238	12	19
# 20	944437	956266	599251	1343193	11	20
# 21	957854	911126	587025	1366863	10	21
# 22	980836	946003	586359	1380741	9	22
# 23	1026015	1014323	635567	1380741	8	23
# 24	1077146	1082644	700896	1380060	7	24
# 25	1153385	1175759	856073	1370801	6	25
# 26	1230937	1268875	1043622	1363627	5	26
# 27	1302170	1332396	1196515	1347375	4	27
# 28	1375346	1383412	1346709	1395917	3	28
# 29	1428581	1428581	1395917	1461246	2	29

# with additional 18 year forecast
# years	avg	median	min	max	count	years
# 1     51550   53970   -302479 224059  48      1
# 2     103904  113077  -302479 431520  47      2
# 3     164040  132894  -287386 623614  46      3
# 4     221884  218723  -255370 801479  45      4
# 5     278012  314052  -154208 966169  44      5
# 6     332450  327440  -130315 966169  43      6
# 7     385466  394833  1173    913384  42      7
# 8     437069  461968  31471   857859  41      8
# 9     490904  534612  80679   852562  40      9
# 10    548246  552201  146008  1013467 39      10
# 11    614045  624286  219896  1162454 38      11
# 12    676403  693143  312584  1300405 37      12
# 13    735493  653630  388835  1428137 36      13
# 14    788891  733592  438043  1428137 35      14
# 15    840712  800190  496776  1387198 34      15
# 16    891806  897614  562105  1384319 33      16
# 17    944257  916944  620391  1315644 32      17
# 18    996974  949239  616169  1393478 31      18
# 19    1054888 1058015 599917  1580375 30      19
# 20    1112165 1111064 599251  1733268 29      20
# 21    1167140 1116476 587025  1920817 28      21
# 22    1222495 1202745 586359  2075994 27      22
# 23    1282200 1287695 635567  2028286 26      23
# 24    1337787 1330477 700896  2063102 25      24
# 25    1393528 1359239 856073  1907815 24      25
# 26    1449444 1413306 1043622 1820624 23      26
# 27    1500335 1495092 1196515 1921786 22      27
# 28    1556187 1518145 1251134 1952099 21      28
# 29    1612639 1599449 1234882 2077167 20      29
# 30    1665613 1664713 1234216 2214785 19      30
# 31    1717011 1756702 1234216 2273518 18      31
# 32    1777426 1829403 1283424 2273430 17      32
# 33    1839349 1842033 1348753 2295890 16      33
# 34    1905614 1881028 1476485 2464306 15      34
# 35    1972228 1939571 1614436 2545760 14      35
# 36    2036750 1983570 1713102 2630882 13      36
# 37    2085576 2052434 1696850 2658837 12      37
# 38    2121247 2098107 1696184 2682507 11      38
# 39    2138162 2155291 1696184 2696385 10      39
# 40    2181796 2247751 1745392 2695704 9       40
# 41    2239978 2312362 1810721 2686445 8       41
# 42    2312801 2383966 1975411 2679271 7       42
# 43    2382564 2380379 2153276 2663019 6       43
# 44    2446511 2376792 2345370 2662353 5       44
# 45    2496201 2456685 2359874 2711561 4       45
# 46    2515282 2409082 2359874 2776890 3       46
# 47    2441746 2441746 2409082 2474411 2       47