How accurate is this home price forecast?

Across the 51 state-level regions with enough history to test (the 50 states plus the District of Columbia), 51 of 51 actual prices landed inside the model's ±1σ band in the one-year hindcast, and the median absolute error was 1.5%. The hindcast refits the model on data through May 31, 2025, hides the following twelve months, then compares the projection to what actually printed. A one-year test is a sanity check on the shape of the model, not proof that the five-year path is right — there is not enough monthly history here to test a five-year horizon.

Which state is forecast to grow fastest through 2031?

Connecticut carries the fastest projected path: the median line runs from $498,000 as of May 31, 2026 to $661,099 in May 2031, a +32.8% total change, with a year-5 band of $386k to $1.13M. Read the band, not the midpoint. Texas sits at the other end at +12.4% on the median path.

How is the forecast calculated?

Base growth is half the state's trailing Redfin CAGR and half the FHFA 1991–2024 long-run CAGR for that state, an empirical-Bayes shrinkage that keeps a single hot or cold stretch from running the projection. forecast.js asks for a 60-month CAGR first, but the trend carries 60 monthly observations, which reach only 59 months back from the last print, so that lookback returns null on every state file here and the blend falls back to the 36-month CAGR (May 31, 2023 to May 31, 2026), i.e. 3 years, not five. Volatility is half the observed monthly-return σ and half the FHFA σ. The band is applied as exp(±σ×T) in log space, so its half-width grows linearly with the horizon rather than with √T — it is a true ±1σ interval (about 68%) at year 1 and roughly ±2.2σ (about 97%) by year 5. The national long-run anchor is 4.40% a year. Full detail is in the method block on this page, and the code is src/lib/forecast.js.

Is this forecast free?

Yes. No signup, no email wall, no subscription. The same state rows behind this table are served as JSON at /api/states/index.json, and reuse terms are at /fair-use/.

Home Price Forecast by State

A five-year median-price projection for every state, each one shown with its confidence band and its own one-year hindcast error, so you can see how the model did before you decide what to make of it. The projection centers on a blend of each state's trailing Redfin growth and its FHFA long-run rate — it is not a prediction that prices will do any particular thing. Market figures are as of May 31, 2026 (Redfin month-end); the pipeline last ran October 8, 2026. Free, no signup, no paywall. Same numbers available as JSON at /api/states/index.json.

Fastest projected 5-yr path Connecticut +32.8% $498,000 → $661,099 · band $386k–$1.13M

Slowest projected 5-yr path Texas +12.4% $356,100 → $400,205 · band $299k–$535k

1-year hindcast 51 of 51 inside the ±1σ band median absolute error 1.5% · fit through May 31, 2025, next 12 months held out

Five-year home price forecast by state

All 51 state files carry a 60-month Redfin trend, so every one produces a projection. Sorted by total change on the median path from May 31, 2026 to May 2031. The band column is the honest part, and it is wider than "±1σ" suggests: forecast.js widens the band linearly with the horizon, so the year-5 band spans ±2.2σ (about 97% coverage under a lognormal assumption), and in high-volatility states it is very wide. A midpoint quoted without its band is not a forecast.

#	State	Median price (May 2026)	Year 1 median (May 2027)	Year 3 median (May 2029)	Year 5 median (May 2031)	5-yr change (median path)	Year-5 band (±2.2σ)	Blended annual growth	1-yr hindcast error
1	Connecticut	$498,000	$527,032	$590,271	$661,099	+32.8%	$386k – $1.13M	5.83%	+1.3%
2	New Jersey	$579,900	$611,508	$679,985	$756,131	+30.4%	$526k – $1.09M	5.45%	-1.9%
3	New Hampshire	$537,900	$567,065	$630,226	$700,421	+30.2%	$470k – $1.04M	5.42%	-0.6%
4	Wisconsin	$361,600	$380,586	$421,600	$467,034	+29.2%	$319k – $683k	5.25%	+1.3%
5	New York	$620,500	$651,764	$719,098	$793,387	+27.9%	$521k – $1.21M	5.04%	+1.4%
6	California	$887,400	$931,487	$1,026,340	$1,130,852	+27.4%	$704k – $1.82M	4.97%	+0.1%
7	Maine	$439,200	$461,001	$507,902	$559,575	+27.4%	$337k – $928k	4.96%	-2.4%
8	Illinois	$337,900	$354,631	$390,620	$430,261	+27.3%	$284k – $651k	4.95%	+1.8%
9	Pennsylvania	$330,200	$346,508	$381,580	$420,202	+27.3%	$271k – $651k	4.94%	+1.2%
10	Rhode Island	$536,900	$562,786	$618,364	$679,429	+26.5%	$422k – $1.09M	4.82%	-2.1%
11	West Virginia	$265,200	$277,963	$305,363	$335,463	+26.5%	$229k – $492k	4.81%	+4.5%
12	Vermont	$448,400	$469,899	$516,038	$566,707	+26.4%	$296k – $1.09M	4.79%	-3.7%
13	Delaware	$384,500	$402,778	$441,982	$485,001	+26.1%	$308k – $763k	4.75%	+1.6%
14	North Dakota	$311,200	$325,890	$357,382	$391,918	+25.9%	$204k – $754k	4.72%	+0.5%
15	Massachusetts	$688,100	$719,223	$785,757	$858,445	+24.8%	$547k – $1.35M	4.52%	-3.6%
16	Missouri	$297,500	$310,942	$339,675	$371,063	+24.7%	$251k – $549k	4.52%	+0.3%
17	New Mexico	$395,500	$413,345	$451,488	$493,151	+24.7%	$194k – $1.25M	4.51%	+7.4%
18	Ohio	$282,600	$295,200	$322,109	$351,471	+24.4%	$236k – $524k	4.46%	+1.4%
19	Nevada	$481,200	$502,387	$547,601	$596,884	+24.0%	$393k – $907k	4.40%	+0.7%
20	Michigan	$297,900	$310,916	$338,680	$368,923	+23.8%	$229k – $595k	4.37%	+0.6%
21	Alaska	$427,100	$445,260	$483,929	$525,956	+23.1%	$349k – $793k	4.25%	+0.4%
22	Kansas	$316,300	$329,626	$357,985	$388,785	+22.9%	$246k – $615k	4.21%	-0.9%
23	Virginia	$499,300	$520,259	$564,854	$613,270	+22.8%	$399k – $943k	4.20%	+0.2%
24	Maryland	$477,300	$497,273	$539,761	$585,879	+22.7%	$393k – $873k	4.18%	+2.0%
25	District of Columbia	$740,000	$770,382	$834,939	$904,906	+22.3%	$417k – $1.96M	4.11%	+1.6%
26	Tennessee	$413,200	$429,927	$465,438	$503,883	+21.9%	$358k – $708k	4.05%	-0.1%
27	Kentucky	$284,400	$295,662	$319,541	$345,349	+21.4%	$244k – $488k	3.96%	-1.8%
28	Utah	$560,200	$582,064	$628,385	$678,392	+21.1%	$478k – $963k	3.90%	-3.0%
29	Iowa	$258,700	$268,773	$290,111	$313,143	+21.0%	$225k – $435k	3.89%	-1.7%
30	Idaho	$503,400	$522,797	$563,861	$608,151	+20.8%	$395k – $937k	3.85%	-0.6%
31	Alabama	$312,600	$324,256	$348,887	$375,390	+20.1%	$268k – $526k	3.73%	+4.2%
32	Colorado	$617,000	$639,908	$688,306	$740,365	+20.0%	$499k – $1.10M	3.71%	-2.0%
33	Indiana	$287,300	$297,883	$320,234	$344,262	+19.8%	$238k – $498k	3.68%	+0.5%
34	Wyoming	$464,500	$481,541	$517,520	$556,188	+19.7%	$142k – $2.18M	3.67%	+17.3%
35	Arkansas	$275,500	$285,576	$306,848	$329,704	+19.7%	$229k – $475k	3.66%	-1.1%
36	South Dakota	$346,600	$359,180	$385,725	$414,232	+19.5%	$266k – $645k	3.63%	+0.2%
37	Washington	$651,800	$674,798	$723,258	$775,198	+18.9%	$510k – $1.18M	3.53%	-2.5%
38	Minnesota	$372,300	$385,404	$413,012	$442,598	+18.9%	$313k – $626k	3.52%	-1.5%
39	Nebraska	$319,100	$330,306	$353,913	$379,207	+18.8%	$266k – $542k	3.51%	-2.1%
40	Mississippi	$284,300	$294,029	$314,499	$336,393	+18.3%	$220k – $515k	3.42%	+2.4%
41	South Carolina	$394,000	$407,341	$435,392	$465,376	+18.1%	$331k – $653k	3.39%	-2.2%
42	Montana	$528,600	$546,204	$583,189	$622,680	+17.8%	$377k – $1.03M	3.33%	-2.6%
43	Hawaii	$741,300	$765,455	$816,152	$870,206	+17.4%	$518k – $1.46M	3.26%	-3.6%
44	North Carolina	$397,600	$410,389	$437,213	$465,791	+17.2%	$361k – $601k	3.22%	-1.1%
45	Oregon	$525,500	$541,920	$576,316	$612,894	+16.6%	$436k – $862k	3.12%	-2.5%
46	Florida	$421,500	$434,620	$462,099	$491,315	+16.6%	$358k – $675k	3.11%	-0.1%
47	Oklahoma	$264,600	$272,827	$290,058	$308,376	+16.5%	$216k – $441k	3.11%	-1.6%
48	Louisiana	$269,000	$277,268	$294,574	$312,960	+16.3%	$222k – $441k	3.07%	+0.9%
49	Georgia	$389,000	$400,782	$425,426	$451,587	+16.1%	$326k – $626k	3.03%	-1.1%
50	Arizona	$453,800	$467,437	$495,953	$526,209	+16.0%	$372k – $744k	3.01%	-1.4%
51	Texas	$356,100	$364,514	$381,943	$400,205	+12.4%	$299k – $535k	2.36%	+0.8%

Housing market crash risk by state

The same 0–100 composite the dashboard uses, with all five inputs shown rather than rolled into a single opaque number. Each sub-score is normalized 0–100 where 100 is maximum downside pressure, then weighted 30/20/20/15/15. 2 states currently reads elevated (70+) and 11 read moderate (50–69). A dash means that input is not published for that region and the composite silently substitutes a neutral 50 for it: District of Columbia. Treat those scores as partial.

#	State	Crash-risk score (0–100)	Reading	Price-to-income (30%)	Price YoY (20%)	Inventory surplus (20%)	DOM spike (15%)	Permits (15%)
1	Washington	73	Elevated downside risk	96	51	100	0	95
2	Hawaii	70	Elevated downside risk	100	51	68	22	88
3	Colorado	68	Moderate downside risk	91	44	85	0	100
4	Montana	68	Moderate downside risk	100	42	73	0	100
5	Utah	65	Moderate downside risk	75	53	87	0	100
6	Oregon	64	Moderate downside risk	87	50	74	0	90
7	Vermont	63	Moderate downside risk	64	42	85	31	93
8	Tennessee	57	Moderate downside risk	76	35	87	20	46
9	Massachusetts	55	Moderate downside risk	94	46	88	2	0
10	California	53	Moderate downside risk	100	33	66	1	23
11	New Hampshire	53	Moderate downside risk	61	27	83	14	74
12	South Carolina	53	Moderate downside risk	69	44	77	19	37
13	Arkansas	50	Moderate downside risk	34	35	73	21	100
14	North Carolina	49	Low downside risk	63	39	94	21	4
15	Arizona	48	Low downside risk	69	48	70	28	0
16	Florida	48	Low downside risk	68	37	63	19	34
17	Maine	48	Low downside risk	75	35	66	0	35
18	New York	48	Low downside risk	100	25	37	39	0
19	Nevada	46	Low downside risk	82	34	60	19	0
20	Kentucky	45	Low downside risk	30	39	73	22	70
21	New Jersey	44	Low downside risk	64	32	40	3	65
22	Idaho	42	Low downside risk	93	44	27	0	0
23	District of Columbia	41	Low downside risk	—	23	69	0	—
24	Rhode Island	39	Low downside risk	78	34	42	3	1
25	Texas	39	Low downside risk	33	40	75	7	33
26	Connecticut	38	Low downside risk	52	14	25	0	100
27	Minnesota	38	Low downside risk	21	44	68	0	65
28	Maryland	37	Low downside risk	34	23	82	13	26
29	Mississippi	37	Low downside risk	48	21	61	18	24
30	New Mexico	37	Low downside risk	82	0	53	9	0
31	Virginia	37	Low downside risk	57	30	71	0	0
32	Alaska	36	Low downside risk	37	29	22	0	100
33	Louisiana	36	Low downside risk	28	34	41	0	86
34	Georgia	35	Low downside risk	49	42	55	4	0
35	Oklahoma	35	Low downside risk	19	40	65	5	50
36	Alabama	33	Low downside risk	44	14	70	15	3
37	Michigan	33	Low downside risk	20	27	54	0	76
38	Wyoming	33	Low downside risk	77	0	35	19	1
39	Delaware	32	Low downside risk	33	21	75	21	0
40	West Virginia	31	Low downside risk	31	11	47	6	62
41	North Dakota	30	Low downside risk	17	20	28	4	99
42	South Dakota	29	Minimal downside risk	37	28	46	0	20
43	Wisconsin	29	Minimal downside risk	37	17	53	9	14
44	Indiana	27	Minimal downside risk	17	29	50	0	38
45	Ohio	27	Minimal downside risk	16	23	59	9	26
46	Iowa	25	Minimal downside risk	1	39	41	3	57
47	Missouri	25	Minimal downside risk	23	27	65	0	0
48	Kansas	22	Minimal downside risk	24	37	33	0	4
49	Pennsylvania	22	Minimal downside risk	24	23	46	5	0
50	Illinois	19	Minimal downside risk	18	21	39	10	0
51	Nebraska	19	Minimal downside risk	22	36	16	10	0

Method, limits, and common questions

Base growth = 0.5 × the state's trailing CAGR from the Redfin median-sale-price trend, + 0.5 × the FHFA long-run state CAGR. That is empirical-Bayes shrinkage toward a prior: it keeps one hot stretch or one bad year from carrying a five-year projection. Across the 51 regions the blended rate runs from 2.36% to 5.83% a year.
The trailing leg is 36 months, not 60. forecast.js asks for a 60-month CAGR first, but the trend carries 60 monthly observations, which reach only 59 months back from the last print, so that lookback returns null on every state file here and the blend falls back to the 36-month CAGR (May 31, 2023 to May 31, 2026), i.e. 3 years, not five. The variable it feeds is still named trailingCAGR5y in src/lib/forecast.js, which is misleading in the code and would be misleading here if we repeated it. Practical effect: the observed half of the blend is a 3-year rate, so it reacts faster to the recent market than a true five-year rate would.
Confidence band. σ is 0.5 × the observed monthly-log-return volatility of the state's own trend + 0.5 × the FHFA long-run σ for that state. Read the implementation before you read the band: projectPrices() computes σ_T = σ×√T and then applies it as exp(±σ_T×√T), so the half-width in log space is σ×T. It grows linearly with the horizon instead of with √T, which means the band is a true ±1σ interval (about 68% under a lognormal assumption) only at year 1, and by year 5 it spans ±2.2σ, about 97%. That is wider than the √T scaling a random walk implies. It is not a floor and not a ceiling.
Horizon is hard-capped at five years in the library itself. Anything longer would be extrapolation past what 60 months of monthly data can support.
The FHFA anchor table is external and hard-coded. LONGRUN_HPI_CAGR_FHFA_1991_2024 and HPI_ANNUAL_VOL_FHFA_1991_2024 in src/lib/forecast.js are literal tables attributed to the FHFA All-Transactions House Price Index, 1991Q1–2024Q4. They were not re-derived by this pipeline — the data this site collects only carries FHFA HPI back to 2016 nationally. Audit them against the source: fhfa.gov/data/hpi/datasets. The national anchor is 4.40%/yr.
Hindcast = refit the whole model on the trend minus its last 12 months (cutoff May 31, 2025), project one year forward, then compare to what actually printed. Error is (actual − projected) ÷ projected, so a positive number means the market came in above the projection. Result: 51 of 51 inside the ±1σ band, median absolute error 1.5%. A one-year test cannot validate a five-year horizon; it validates the shape.
Nominal, not inflation-adjusted. Every dollar figure here is nominal. The median state sits at +21.9% over five years on the median path, and a nominal gain of that size is a different thing from a real one.
Crash-risk weights are 30% price-to-income, 20% price YoY, 20% inventory surplus vs long-run average, 15% days-on-market spike, 15% building-permit YoY. Those weights live as a module-private constant in src/lib/crash-risk.js, so they are restated here by hand. Every source behind the factor choices, 6 of them, is listed on the crash-risk methodology page. It is a relative-ranking composite, not a forecast, and it contains no macro inputs — Fed policy, employment, and recession risk are all outside it.
Missing inputs are not penalized, they are averaged. Each normalizer returns a neutral 50 when its input is null, so a region missing an input drifts toward the middle instead of dropping out. That currently affects District of Columbia, shown with a dash in the affected columns.
Dates. Market figures are as of May 31, 2026, the Redfin month-end in the data, identical across all 51 state files. October 8, 2026 is when the pipeline last ran, which is a different thing and is not the date of the data.
How accurate is this home price forecast? Across the 51 state-level regions with enough history to test (the 50 states plus the District of Columbia), 51 of 51 actual prices landed inside the model's ±1σ band in the one-year hindcast, and the median absolute error was 1.5%. The hindcast refits the model on data through May 31, 2025, hides the following twelve months, then compares the projection to what actually printed. A one-year test is a sanity check on the shape of the model, not proof that the five-year path is right — there is not enough monthly history here to test a five-year horizon.
Which state is forecast to grow fastest through 2031? Connecticut carries the fastest projected path: the median line runs from $498,000 as of May 31, 2026 to $661,099 in May 2031, a +32.8% total change, with a year-5 band of $386k to $1.13M. Read the band, not the midpoint. Texas sits at the other end at +12.4% on the median path.
How is the forecast calculated? Base growth is half the state's trailing Redfin CAGR and half the FHFA 1991–2024 long-run CAGR for that state, an empirical-Bayes shrinkage that keeps a single hot or cold stretch from running the projection. forecast.js asks for a 60-month CAGR first, but the trend carries 60 monthly observations, which reach only 59 months back from the last print, so that lookback returns null on every state file here and the blend falls back to the 36-month CAGR (May 31, 2023 to May 31, 2026), i.e. 3 years, not five. Volatility is half the observed monthly-return σ and half the FHFA σ. The band is applied as exp(±σ×T) in log space, so its half-width grows linearly with the horizon rather than with √T — it is a true ±1σ interval (about 68%) at year 1 and roughly ±2.2σ (about 97%) by year 5. The national long-run anchor is 4.40% a year. Full detail is in the method block on this page, and the code is src/lib/forecast.js.
Is this forecast free? Yes. No signup, no email wall, no subscription. The same state rows behind this table are served as JSON at /api/states/index.json, and reuse terms are at /fair-use/. If you want to see how this compares to a paid subscription product, we wrote that up at /compare-vs-reventure/.
Related: per-state charts and the interactive version of this projection live on each state page. See also market movers, the permit supply pipeline, and county distress watch.

Data as of May 31, 2026 (Redfin month-end); pipeline last run October 8, 2026. Sources: Redfin, FRED (St. Louis Fed), FHFA, U.S. Census Bureau, plus the FHFA HPI datasets for the long-run CAGR and volatility anchors. Forecast code: src/lib/forecast.js. Crash-risk code: src/lib/crash-risk.js. Full source list at /sources/. This is published information, not investment or lending advice.