Model Lab · evidence for your home search

Understand the market behind your shortlist.

Compare buying conditions, test your budget, and explore model predictions alongside their actual outcomes.

Market tools and forecast evaluations cover the eight research cities. Compare prices and historical changes across all 25 cities →

Market conditions · observed history

How competitive is the market?

Compare homes for sale and time to reach a contract. Look at the same season a year earlier to put today’s conditions in context.

Latest captured market, rent and mortgage update

Checking the source archive… The reviewed history below keeps its published dates.

Market yearPeriod ending

Jun 2026–Aug 2026Three-month rolling window

Explore the history

Mar 2012Aug 2026

Single-family homes · Jun 2026–Aug 2026. Revised data retrieved 2026-09-16; this does not recreate what was published at the time.

San Bruno

Inventory and contract speed give a mixed picture

Homes for sale at period end: 15
Median days to a contract: 13
Closed sales over three months: 56
Median sale price: $1,457,146

See the year-earlier comparison

Aug 2026 versus Aug 2025; each sales measure spans three months.
Measure	Selected period	Year earlier
Inventory	15	15
Days to contract	13	18
Closed sales	56	66
New listings	51	59
Pending sales	46	68

South San Francisco

Inventory and contract speed give a mixed picture

Homes for sale at period end: 28
Median days to a contract: 14
Closed sales over three months: 70
Median sale price: $1,301,737

See the year-earlier comparison

Aug 2026 versus Aug 2025; each sales measure spans three months.
Measure	Selected period	Year earlier
Inventory	28	21
Days to contract	14	15
Closed sales	70	53
New listings	71	65
Pending sales	58	63

Homes available over time

Homes counted in each named geography. 2012-03 to 2026-08.

San Bruno 15South San Francisco 28

View exact values (174 periods)

Homes available over time: homes; missing values are unavailable
Month	San Bruno	South San Francisco
2026-08	15	28
2026-07	17	15
2026-06	20	17
2026-05	20	17
2026-04	23	25
2026-03	20	28
2026-02	18	21
2026-01	11	19
2025-12	6	7
2025-11	11	10
2025-10	13	15
2025-09	17	27
2025-08	15	21
2025-07	22	32
2025-06	26	29
2025-05	28	30
2025-04	21	22
2025-03	16	19
2025-02	14	12
2025-01	13	17
2024-12	9	8
2024-11	24	22
2024-10	30	22
2024-09	27	33
2024-08	28	20
2024-07	26	22
2024-06	25	19
2024-05	18	15
2024-04	7	12
2024-03	10	9
2024-02	13	10
2024-01	12	12
2023-12	6	10
2023-11	14	16
2023-10	20	27
2023-09	21	25
2023-08	13	15
2023-07	11	24
2023-06	13	19
2023-05	16	17
2023-04	11	19
2023-03	10	13
2023-02	9	15
2023-01	13	15
2022-12	13	12
2022-11	21	26
2022-10	29	28
2022-09	29	28
2022-08	29	28
2022-07	28	38
2022-06	23	42
2022-05	18	21
2022-04	15	18
2022-03	12	24
2022-02	13	17
2022-01	14	10
2021-12	8	4
2021-11	9	17
2021-10	18	23
2021-09	21	27
2021-08	20	25
2021-07	24	20
2021-06	22	23
2021-05	17	17
2021-04	15	14
2021-03	9	9
2021-02	15	10
2021-01	11	18
2020-12	11	13
2020-11	19	24
2020-10	26	24
2020-09	28	36
2020-08	26	44
2020-07	34	36
2020-06	39	41
2020-05	32	33
2020-04	38	23
2020-03	22	16
2020-02	24	17
2020-01	21	13
2019-12	17	13
2019-11	23	31
2019-10	32	40
2019-09	42	27
2019-08	32	28
2019-07	31	43
2019-06	25	48
2019-05	28	33
2019-04	28	30
2019-03	16	20
2019-02	18	20
2019-01	17	15
2018-12	16	16
2018-11	25	29
2018-10	24	28
2018-09	26	44
2018-08	22	33
2018-07	22	34
2018-06	14	20
2018-05	14	20
2018-04	16	24
2018-03	15	13
2018-02	12	7
2018-01	10	13
2017-12	6	4
2017-11	15	10
2017-10	18	17
2017-09	13	29
2017-08	18	18
2017-07	20	27
2017-06	19	11
2017-05	20	31
2017-04	23	25
2017-03	18	19
2017-02	9	15
2017-01	7	22
2016-12	7	19
2016-11	14	34
2016-10	18	40
2016-09	33	40
2016-08	36	38
2016-07	35	46
2016-06	39	39
2016-05	33	20
2016-04	22	31
2016-03	15	27
2016-02	13	38
2016-01	14	21
2015-12	14	18
2015-11	24	30
2015-10	27	40
2015-09	21	38
2015-08	24	30
2015-07	22	30
2015-06	17	26
2015-05	17	25
2015-04	21	21
2015-03	18	19
2015-02	15	22
2015-01	10	19
2014-12	10	15
2014-11	17	26
2014-10	22	48
2014-09	28	43
2014-08	22	46
2014-07	27	52
2014-06	27	44
2014-05	21	42
2014-04	18	22
2014-03	21	29
2014-02	22	33
2014-01	19	18
2013-12	14	19
2013-11	26	41
2013-10	30	48
2013-09	35	41
2013-08	29	39
2013-07	20	37
2013-06	15	26
2013-05	25	24
2013-04	19	28
2013-03	21	28
2013-02	19	34
2013-01	19	31
2012-12	20	31
2012-11	22	38
2012-10	24	37
2012-09	20	36
2012-08	21	46
2012-07	22	41
2012-06	28	44
2012-05	38	54
2012-04	38	54
2012-03	38	46

Time to reach a contract

Median days from listing to contract for the named series. 2012-03 to 2026-08.

San Bruno 13South San Francisco 14

View exact values (174 periods)

Time to reach a contract: days; missing values are unavailable
Month	San Bruno	South San Francisco
2026-08	13	14
2026-07	14	13
2026-06	14	12
2026-05	12	14
2026-04	11	14
2026-03	8	14
2026-02	12	15
2026-01	14	16
2025-12	17	15
2025-11	15	15
2025-10	15	15
2025-09	18	15
2025-08	18	15
2025-07	14	13
2025-06	12	13
2025-05	11	12
2025-04	12	13
2025-03	13	15
2025-02	29	15
2025-01	29	21
2024-12	16	16
2024-11	12	15
2024-10	14	15
2024-09	16	14
2024-08	13	13
2024-07	12	13
2024-06	11	9
2024-05	11	9
2024-04	15	9
2024-03	19	12
2024-02	22	24
2024-01	25	16
2023-12	20	21
2023-11	12	14
2023-10	12	15
2023-09	12	15
2023-08	9	13
2023-07	9	13
2023-06	9	12
2023-05	13	12
2023-04	14	15
2023-03	16	24
2023-02	26	32
2023-01	26	25
2022-12	22	21
2022-11	26	22
2022-10	22	24
2022-09	25	27
2022-08	15	17
2022-07	12	13
2022-06	11	12
2022-05	10	11
2022-04	10	11
2022-03	11	12
2022-02	12	14
2022-01	12	15
2021-12	12	14
2021-11	12	14
2021-10	12	14
2021-09	11	14
2021-08	10	12
2021-07	9	9
2021-06	8	8
2021-05	8	9
2021-04	9	11
2021-03	13	12
2021-02	14	13
2021-01	18	14
2020-12	17	16
2020-11	18	18
2020-10	20	26
2020-09	28	29
2020-08	31	31
2020-07	36	30
2020-06	27	23
2020-05	25	21
2020-04	19	14
2020-03	29	28
2020-02	37	32
2020-01	42	39
2019-12	39	33
2019-11	32	32
2019-10	30	26
2019-09	23	26
2019-08	23	26
2019-07	18	27
2019-06	14	16
2019-05	15	15
2019-04	15	17
2019-03	26	19
2019-02	26	26
2019-01	29	21
2018-12	25	30
2018-11	23	23
2018-10	16	19
2018-09	13	14
2018-08	13	14
2018-07	14	14
2018-06	14	13
2018-05	14	13
2018-04	14	12
2018-03	14	12
2018-02	18	12
2018-01	16	16
2017-12	16	15
2017-11	15	16
2017-10	15	12
2017-09	15	13
2017-08	16	13
2017-07	16	14
2017-06	15	14
2017-05	15	16
2017-04	13	19
2017-03	15	29
2017-02	15	41
2017-01	21	40
2016-12	29	37
2016-11	34	33
2016-10	31	29
2016-09	26	25
2016-08	19	18
2016-07	15	15
2016-06	15	15
2016-05	14	15
2016-04	15	17
2016-03	16	19
2016-02	18	33
2016-01	28	34
2015-12	26	32
2015-11	24	24
2015-10	16	18
2015-09	14	16
2015-08	13	14
2015-07	14	13
2015-06	13	12
2015-05	12	13
2015-04	11	13
2015-03	15	13
2015-02	21	18
2015-01	28	40
2014-12	22	32
2014-11	16	27
2014-10	15	20
2014-09	14	17
2014-08	13	16
2014-07	13	14
2014-06	13	15
2014-05	13	14
2014-04	13	14
2014-03	15	16
2014-02	22	25
2014-01	25	24
2013-12	17	20
2013-11	15	17
2013-10	13	18
2013-09	14	16
2013-08	13	14
2013-07	14	14
2013-06	12	14
2013-05	12	14
2013-04	12	14
2013-03	14	17
2013-02	14	15
2013-01	15	17
2012-12	16	16
2012-11	15	17
2012-10	16	16
2012-09	16	16
2012-08	15	16
2012-07	16	15
2012-06	20	22
2012-05	31	31
2012-04	35	51
2012-03	34	41

Use this when you review a listing

More available homes and longer marketing times can give you more options to investigate. Ask how the actual property compares with other listings in its price range. These city observations do not predict your offer’s outcome.

What the data and research support

Redfin’s sales and prices cover completed sales; contract time covers homes going under contract. Those populations differ. Inventory is the count at period end. Rolling windows overlap, so adding them would double-count activity. A changing median price can reflect the mix of homes sold.

Year-earlier differences are calculated from retained levels. The two-direction summary appears only when both periods have at least ten closed sales and the two required measures. It describes the observations, with no fitted score or forecast.

Listing behavior motivated this view. The Federal Reserve listing study and buyer-search study use richer inputs and different geographies. This view is not a replication of those forecasts.

Redfin definitions Research and data coverage Download the evidence

Market outlook · experimental

Will homes take longer to reach a contract?

Apply four ways of thinking about the market. Compare what each predicts for the next quarter, then inspect its past misses.

One clear target: days from listing to contract

Longer contract times may give buyers more time to compare listings. This forecasts a city’s median for a calendar quarter, not a particular home’s selling time or price.

No added model earned selection. Same season remains the baseline. See the historical comparison below.

Forecast yearForecast quarter

Oct–Dec 2026Frozen on 2026-09-16

Current blend: Same season 100%.

Apply models and adjust their weights

Weights express your scenario. They are normalized to 100%; they are not probabilities. Changing them recomputes the historical comparison too. This experiment is separate from the annual price-forecast weights.

Current pace

Contract time stays at the latest observed pace.

The most recent eligible three-month window.Current pace weight0 (0% of blend)

Same season

Next quarter resembles the same quarter last year.

The same calendar quarter one year earlier. The retained baseline.Same season weight100 (100% of blend)

Contract-time trend

Recent changes in contract speed help explain what comes next.

Changes in contract days over three months and one year; fitted on other California cities.Contract-time trend weight0 (0% of blend)

Supply & demand

Homes available and the flow of new listings and contracts add information.

Contract-time trends, inventory relative to sales, inventory and sales growth, and pending contracts relative to new listings.Supply & demand weight0 (0% of blend)

San Bruno

Your scenario · Oct–Dec 202617 days

Latest input: 13 days · window ending 2026-08-31. Target: 2026-10-01–2026-12-31.

Check the live outcome record below for progress. The frozen study predictions remain unchanged when you adjust these weights.

See every model’s prediction

San Bruno, Oct–Dec 2026, median days to contract
Current pace	13 days
Same season	17 days
Contract-time trend	14.8 days
Supply & demand	17.5 days
Equal blend	15.6 days

Latest training outcome: 2026-06-30. 14,606 peer training rows, 49 quarter origins. Inputs stop at 2026-08-31.

South San Francisco

Your scenario · Oct–Dec 202615 days

Latest input: 14 days · window ending 2026-08-31. Target: 2026-10-01–2026-12-31.

Check the live outcome record below for progress. The frozen study predictions remain unchanged when you adjust these weights.

See every model’s prediction

South San Francisco, Oct–Dec 2026, median days to contract
Current pace	14 days
Same season	15 days
Contract-time trend	14.7 days
Supply & demand	18 days
Equal blend	15.4 days

Latest training outcome: 2026-06-30. 14,606 peer training rows, 49 quarter origins. Inputs stop at 2026-08-31.

How large were the misses?

The selection period chose the baseline. Later history checks that decision without changing it. Both are exploratory replays using today’s revised source history and an assumed one-month reporting allowance.

Evaluation period

Your cities and weights · average miss4.7 days

Same-season average miss · same rows4.7 days

Largest scenario miss14 days

34 comparable city/quarter rows across 17 quarter origins and 2 selected cities. Average miss is mean absolute error in days; it is not a confidence interval.

San Bruno: scenario versus observed contract time

Median days from listing to contract for the named series. 2022 Q2 to 2026 Q2.

Your scenario 12Observed 14

View exact values (17 periods)

San Bruno: scenario versus observed contract time: days; missing values are unavailable
Quarter	Your scenario	Observed
2026 Q2	12	14
2026 Q1	13	8
2025 Q4	16	17
2025 Q3	16	18
2025 Q2	11	12
2025 Q1	19	13
2024 Q4	20	16
2024 Q3	12	16
2024 Q2	9	11
2024 Q1	16	19
2023 Q4	22	20
2023 Q3	25	12
2023 Q2	11	9
2023 Q1	11	16
2022 Q4	12	22
2022 Q3	11	25
2022 Q2	8	11

The CSV includes the full available replay and forward scenario with your weights. It is a scenario export, not a new registered forecast.

Full study comparison and selection rule

All eight covered cities, single-family homes. Average absolute miss in days.
Model	Selection period	Later replay
Current pace	7 days	5.8 days
Same season	9.8 days	5.2 days
Contract-time trend	7.6 days	5.7 days
Supply & demand	6.6 days	5.3 days
Equal blend	6.7 days	4.7 days

No candidate passed the prespecified sample, improvement and consistency gate. Retain same-season baseline. A candidate must improve average error at least 10% against both simple baselines, beat same season in at least 7 of 12 origins, and meet the minimum local sample. The sample gate passed.

The selection period has 96 comparable rows and 12 origins; later replay has 136 rows and 17 origins. There are no random city-row splits, and training targets end before each input cutoff. Other California cities can still share regional shocks.

Coefficient fitting uses 299 other California cities; our eight cities are evaluated separately. Neighboring markets are correlated, so city counts overstate independent evidence.

The next live check

October–December 2026: keep the prediction, check the outcome

Thirteen city/home-type forecasts were frozen before October 1. Each week, the data worker captures source evidence. After December 31, it locks each first eligible outcome and reports errors. Thin or missing outcomes remain pending.

Checking the capture record…

Capture status and locked outcomes Latest captured observations and sources

Download the study and source references Frozen October–December predictions What remains untested

Source: Redfin Data Center, retrieved 2026-09-16. Contract-time medians and closed-sales counts describe different transaction populations. The ten-sale quality rule is not a calibrated uncertainty claim. Public 2025 outcomes were already inspected; this is not an untouched historical test.

Rent or buy · your cost scenario

What could owning cost you?

Start with a home price and rent for a comparable home. See the monthly budget, equity built through repayments, and how your assumptions change the longer-term comparison.

Use a dated local price or rent benchmark

Local benchmarks to help you start

Sale prices are for single-family homes. Rental benchmarks include all rental housing and multifamily. They describe different populations; use a comparable property’s rent for your decision.

San Bruno

$1,457,146Median sale price · Jun 2026–Aug 2026

$3,338 / monthCity-wide rental benchmark · Jul 2026

South San Francisco

$1,301,737Median sale price · Jun 2026–Aug 2026

$4,088 / monthCity-wide rental benchmark · Jul 2026

Your assumptions

Enter your own figures, or use a benchmark above. Inputs stay in this page; they are not sent to the server.

Home price ($)Comparable monthly rent ($)Down payment (%)Mortgage interest rate (%)Years you expect to stayAssumed yearly home-price change (%)

Starting mortgage rate: Freddie Mac’s 6.76% national 30-year average, 2026-09-10. Replace it with your lender’s quote, especially for a jumbo loan or different loan term.

Home-price change is your assumption. The starting 0% holds the price flat. It is not a prediction from the research models.

Edit taxes, fees and other assumptions

Taxes, insurance, upkeep and transaction costs start as examples. Check the actual property and your loan terms. Recurring-cost growth is an assumption, not a projection of local tax rules.

Loan term (years)Annual property tax (% of purchase price)Home insurance per month ($)Annual maintenance (% of purchase price)HOA fees per month ($)Mortgage insurance per month ($)Buying costs (% of purchase price)Selling costs (% of sale price)Assumed yearly rent increase (%)Assumed yearly increase in recurring costs (%)Alternative annual return on your cash (%)

Current cost assumptions: 1.2% property tax per year, $250 insurance per month, 1% annual upkeep, $0 HOA per month, 2% buying costs and 5% selling costs. Loan term: 30 years.

Build your comparison

Enter a home price and comparable rent, or use the local benchmarks above.

Start with your budget

What price fits your monthly limit and cash?

Uses the down payment, financing, taxes, upkeep, insurance, HOA and buying fees above. Enter cash set aside for the purchase after keeping your emergency reserves. This estimates a spending limit, not lender approval.

Monthly ownership budget ($)Cash available for the purchase ($)

Enter both budgets and valid cost assumptions to calculate a price limit. Mortgage insurance must be specified when required above.

Explore mortgage rates and purchasing power over time

Freddie Mac PMMS, retrieved from FRED, Federal Reserve Bank of St. Louis. 2,894 weekly observations through 2026-09-10. This national conforming-loan benchmark is not a quote for a Bay Area jumbo borrower. The method changed on November 17, 2022.

Mortgage history year

6.76% on 2026-09-10, the last available observation in 2026. At that fixed rate a $100,000, 30-year loan has a $649 monthly principal-and-interest payment, before all other costs.

National 30-year mortgage rate history

Annual interest rate, percent. 1971-04 to 2026-09.

Monthly average of observed weekly rates 6.7%

View exact values (666 periods)

National 30-year mortgage rate history: percent; missing values are unavailable
Month	Monthly average of observed weekly rates
2026-09	6.73%
2026-08	6.67%
2026-07	6.54%
2026-06	6.49%
2026-05	6.44%
2026-04	6.33%
2026-03	6.18%
2026-02	6.05%
2026-01	6.10%
2025-12	6.19%
2025-11	6.24%
2025-10	6.25%
2025-09	6.35%
2025-08	6.59%
2025-07	6.72%
2025-06	6.82%
2025-05	6.82%
2025-04	6.72%
2025-03	6.65%
2025-02	6.84%
2025-01	6.96%
2024-12	6.71%
2024-11	6.80%
2024-10	6.43%
2024-09	6.18%
2024-08	6.50%
2024-07	6.85%
2024-06	6.92%
2024-05	7.06%
2024-04	6.99%
2024-03	6.82%
2024-02	6.78%
2024-01	6.64%
2023-12	6.81%
2023-11	7.44%
2023-10	7.62%
2023-09	7.20%
2023-08	7.07%
2023-07	6.84%
2023-06	6.71%
2023-05	6.42%
2023-04	6.34%
2023-03	6.54%
2023-02	6.26%
2023-01	6.27%
2022-12	6.36%
2022-11	6.80%
2022-10	6.90%
2022-09	6.11%
2022-08	5.22%
2022-07	5.41%
2022-06	5.52%
2022-05	5.23%
2022-04	4.98%
2022-03	4.17%
2022-02	3.76%
2022-01	3.45%
2021-12	3.10%
2021-11	3.07%
2021-10	3.07%
2021-09	2.90%
2021-08	2.84%
2021-07	2.87%
2021-06	2.98%
2021-05	2.96%
2021-04	3.06%
2021-03	3.08%
2021-02	2.81%
2021-01	2.73%
2020-12	2.68%
2020-11	2.77%
2020-10	2.83%
2020-09	2.89%
2020-08	2.94%
2020-07	3.02%
2020-06	3.16%
2020-05	3.23%
2020-04	3.31%
2020-03	3.45%
2020-02	3.46%
2020-01	3.62%
2019-12	3.72%
2019-11	3.69%
2019-10	3.69%
2019-09	3.61%
2019-08	3.62%
2019-07	3.77%
2019-06	3.80%
2019-05	4.07%
2019-04	4.14%
2019-03	4.26%
2019-02	4.37%
2019-01	4.46%
2018-12	4.64%
2018-11	4.87%
2018-10	4.83%
2018-09	4.63%
2018-08	4.55%
2018-07	4.53%
2018-06	4.57%
2018-05	4.59%
2018-04	4.47%
2018-03	4.44%
2018-02	4.33%
2018-01	4.03%
2017-12	3.95%
2017-11	3.92%
2017-10	3.90%
2017-09	3.81%
2017-08	3.88%
2017-07	3.97%
2017-06	3.90%
2017-05	4.01%
2017-04	4.04%
2017-03	4.20%
2017-02	4.17%
2017-01	4.15%
2016-12	4.20%
2016-11	3.77%
2016-10	3.47%
2016-09	3.46%
2016-08	3.44%
2016-07	3.44%
2016-06	3.57%
2016-05	3.60%
2016-04	3.60%
2016-03	3.69%
2016-02	3.66%
2016-01	3.87%
2015-12	3.96%
2015-11	3.94%
2015-10	3.80%
2015-09	3.89%
2015-08	3.90%
2015-07	4.05%
2015-06	3.98%
2015-05	3.84%
2015-04	3.67%
2015-03	3.77%
2015-02	3.71%
2015-01	3.67%
2014-12	3.86%
2014-11	4.00%
2014-10	4.04%
2014-09	4.16%
2014-08	4.12%
2014-07	4.13%
2014-06	4.16%
2014-05	4.19%
2014-04	4.34%
2014-03	4.34%
2014-02	4.30%
2014-01	4.43%
2013-12	4.46%
2013-11	4.25%
2013-10	4.19%
2013-09	4.49%
2013-08	4.46%
2013-07	4.37%
2013-06	4.07%
2013-05	3.54%
2013-04	3.45%
2013-03	3.57%
2013-02	3.53%
2013-01	3.41%
2012-12	3.35%
2012-11	3.35%
2012-10	3.38%
2012-09	3.50%
2012-08	3.60%
2012-07	3.55%
2012-06	3.67%
2012-05	3.80%
2012-04	3.91%
2012-03	3.95%
2012-02	3.89%
2012-01	3.92%
2011-12	3.96%
2011-11	3.99%
2011-10	4.07%
2011-09	4.11%
2011-08	4.27%
2011-07	4.54%
2011-06	4.51%
2011-05	4.64%
2011-04	4.84%
2011-03	4.84%
2011-02	4.95%
2011-01	4.75%
2010-12	4.71%
2010-11	4.30%
2010-10	4.23%
2010-09	4.35%
2010-08	4.43%
2010-07	4.56%
2010-06	4.74%
2010-05	4.89%
2010-04	5.10%
2010-03	4.97%
2010-02	4.99%
2010-01	5.03%
2009-12	4.93%
2009-11	4.88%
2009-10	4.95%
2009-09	5.06%
2009-08	5.19%
2009-07	5.22%
2009-06	5.42%
2009-05	4.86%
2009-04	4.81%
2009-03	5.00%
2009-02	5.13%
2009-01	5.05%
2008-12	5.29%
2008-11	6.09%
2008-10	6.20%
2008-09	6.04%
2008-08	6.48%
2008-07	6.43%
2008-06	6.32%
2008-05	6.04%
2008-04	5.92%
2008-03	5.97%
2008-02	5.92%
2008-01	5.76%
2007-12	6.10%
2007-11	6.21%
2007-10	6.38%
2007-09	6.38%
2007-08	6.57%
2007-07	6.70%
2007-06	6.66%
2007-05	6.26%
2007-04	6.18%
2007-03	6.16%
2007-02	6.29%
2007-01	6.22%
2006-12	6.13%
2006-11	6.24%
2006-10	6.36%
2006-09	6.40%
2006-08	6.52%
2006-07	6.76%
2006-06	6.68%
2006-05	6.60%
2006-04	6.51%
2006-03	6.32%
2006-02	6.25%
2006-01	6.15%
2005-12	6.27%
2005-11	6.33%
2005-10	6.06%
2005-09	5.77%
2005-08	5.82%
2005-07	5.70%
2005-06	5.58%
2005-05	5.72%
2005-04	5.86%
2005-03	5.93%
2005-02	5.63%
2005-01	5.71%
2004-12	5.75%
2004-11	5.73%
2004-10	5.72%
2004-09	5.75%
2004-08	5.87%
2004-07	6.06%
2004-06	6.29%
2004-05	6.27%
2004-04	5.83%
2004-03	5.45%
2004-02	5.63%
2004-01	5.71%
2003-12	5.88%
2003-11	5.93%
2003-10	5.95%
2003-09	6.15%
2003-08	6.26%
2003-07	5.63%
2003-06	5.23%
2003-05	5.48%
2003-04	5.81%
2003-03	5.75%
2003-02	5.84%
2003-01	5.92%
2002-12	6.05%
2002-11	6.07%
2002-10	6.11%
2002-09	6.09%
2002-08	6.29%
2002-07	6.49%
2002-06	6.65%
2002-05	6.81%
2002-04	6.99%
2002-03	7.01%
2002-02	6.89%
2002-01	7.00%
2001-12	7.07%
2001-11	6.66%
2001-10	6.62%
2001-09	6.82%
2001-08	6.95%
2001-07	7.13%
2001-06	7.16%
2001-05	7.14%
2001-04	7.08%
2001-03	6.95%
2001-02	7.05%
2001-01	7.03%
2000-12	7.38%
2000-11	7.75%
2000-10	7.79%
2000-09	7.91%
2000-08	8.03%
2000-07	8.15%
2000-06	8.29%
2000-05	8.51%
2000-04	8.15%
2000-03	8.24%
2000-02	8.33%
2000-01	8.21%
1999-12	7.91%
1999-11	7.74%
1999-10	7.85%
1999-09	7.82%
1999-08	7.94%
1999-07	7.63%
1999-06	7.55%
1999-05	7.15%
1999-04	6.92%
1999-03	7.04%
1999-02	6.81%
1999-01	6.79%
1998-12	6.74%
1998-11	6.87%
1998-10	6.71%
1998-09	6.72%
1998-08	6.92%
1998-07	6.95%
1998-06	7.00%
1998-05	7.14%
1998-04	7.14%
1998-03	7.13%
1998-02	7.04%
1998-01	6.99%
1997-12	7.10%
1997-11	7.21%
1997-10	7.29%
1997-09	7.43%
1997-08	7.48%
1997-07	7.50%
1997-06	7.69%
1997-05	7.94%
1997-04	8.14%
1997-03	7.90%
1997-02	7.65%
1997-01	7.82%
1996-12	7.60%
1996-11	7.62%
1996-10	7.92%
1996-09	8.23%
1996-08	8.00%
1996-07	8.25%
1996-06	8.32%
1996-05	8.07%
1996-04	7.93%
1996-03	7.62%
1996-02	7.08%
1996-01	7.03%
1995-12	7.20%
1995-11	7.38%
1995-10	7.47%
1995-09	7.64%
1995-08	7.86%
1995-07	7.61%
1995-06	7.57%
1995-05	7.96%
1995-04	8.32%
1995-03	8.46%
1995-02	8.83%
1995-01	9.15%
1994-12	9.20%
1994-11	9.17%
1994-10	8.93%
1994-09	8.64%
1994-08	8.51%
1994-07	8.61%
1994-06	8.40%
1994-05	8.60%
1994-04	8.32%
1994-03	7.67%
1994-02	7.15%
1994-01	7.06%
1993-12	7.17%
1993-11	7.16%
1993-10	6.83%
1993-09	6.92%
1993-08	7.11%
1993-07	7.21%
1993-06	7.42%
1993-05	7.46%
1993-04	7.47%
1993-03	7.50%
1993-02	7.68%
1993-01	7.99%
1992-12	8.21%
1992-11	8.30%
1992-10	8.09%
1992-09	7.92%
1992-08	7.97%
1992-07	8.13%
1992-06	8.51%
1992-05	8.67%
1992-04	8.85%
1992-03	8.93%
1992-02	8.76%
1992-01	8.43%
1991-12	8.50%
1991-11	8.71%
1991-10	8.86%
1991-09	9.01%
1991-08	9.24%
1991-07	9.57%
1991-06	9.62%
1991-05	9.47%
1991-04	9.49%
1991-03	9.50%
1991-02	9.37%
1991-01	9.64%
1990-12	9.67%
1990-11	10.01%
1990-10	10.18%
1990-09	10.18%
1990-08	10.10%
1990-07	10.04%
1990-06	10.16%
1990-05	10.48%
1990-04	10.37%
1990-03	10.27%
1990-02	10.20%
1990-01	9.89%
1989-12	9.74%
1989-11	9.77%
1989-10	9.95%
1989-09	10.13%
1989-08	9.98%
1989-07	9.88%
1989-06	10.20%
1989-05	10.77%
1989-04	11.05%
1989-03	11.03%
1989-02	10.64%
1989-01	10.73%
1988-12	10.61%
1988-11	10.27%
1988-10	10.30%
1988-09	10.48%
1988-08	10.60%
1988-07	10.43%
1988-06	10.46%
1988-05	10.46%
1988-04	10.20%
1988-03	9.93%
1988-02	9.89%
1988-01	10.38%
1987-12	10.64%
1987-11	10.65%
1987-10	11.26%
1987-09	10.89%
1987-08	10.33%
1987-07	10.28%
1987-06	10.54%
1987-05	10.60%
1987-04	9.83%
1987-03	9.04%
1987-02	9.08%
1987-01	9.20%
1986-12	9.31%
1986-11	9.70%
1986-10	9.97%
1986-09	10.01%
1986-08	10.20%
1986-07	10.51%
1986-06	10.68%
1986-05	10.14%
1986-04	9.94%
1986-03	10.08%
1986-02	10.71%
1986-01	10.89%
1985-12	11.26%
1985-11	11.78%
1985-10	12.13%
1985-09	12.19%
1985-08	12.19%
1985-07	12.03%
1985-06	12.21%
1985-05	12.91%
1985-04	13.19%
1985-03	13.17%
1985-02	12.92%
1985-01	13.07%
1984-12	13.18%
1984-11	13.64%
1984-10	14.13%
1984-09	14.35%
1984-08	14.47%
1984-07	14.67%
1984-06	14.42%
1984-05	13.94%
1984-04	13.65%
1984-03	13.39%
1984-02	13.23%
1984-01	13.37%
1983-12	13.42%
1983-11	13.44%
1983-10	13.53%
1983-09	13.73%
1983-08	13.81%
1983-07	13.42%
1983-06	12.87%
1983-05	12.63%
1983-04	12.78%
1983-03	12.80%
1983-02	13.04%
1983-01	13.25%
1982-12	13.62%
1982-11	13.82%
1982-10	14.61%
1982-09	15.43%
1982-08	16.27%
1982-07	16.82%
1982-06	16.70%
1982-05	16.68%
1982-04	16.89%
1982-03	17.16%
1982-02	17.60%
1982-01	17.48%
1981-12	16.95%
1981-11	17.83%
1981-10	18.45%
1981-09	18.16%
1981-08	17.29%
1981-07	16.83%
1981-06	16.70%
1981-05	16.40%
1981-04	15.58%
1981-03	15.40%
1981-02	15.13%
1981-01	14.90%
1980-12	14.79%
1980-11	14.21%
1980-10	13.79%
1980-09	13.20%
1980-08	12.56%
1980-07	12.19%
1980-06	12.71%
1980-05	14.26%
1980-04	16.33%
1980-03	15.28%
1980-02	13.04%
1980-01	12.88%
1979-12	12.90%
1979-11	12.83%
1979-10	11.64%
1979-09	11.30%
1979-08	11.09%
1979-07	11.09%
1979-06	11.04%
1979-05	10.69%
1979-04	10.50%
1979-03	10.43%
1979-02	10.41%
1979-01	10.39%
1978-12	10.35%
1978-11	10.11%
1978-10	9.86%
1978-09	9.76%
1978-08	9.79%
1978-07	9.74%
1978-06	9.71%
1978-05	9.57%
1978-04	9.36%
1978-03	9.20%
1978-02	9.14%
1978-01	9.02%
1977-12	8.96%
1977-11	8.92%
1977-10	8.92%
1977-09	8.90%
1977-08	8.94%
1977-07	8.94%
1977-06	8.86%
1977-05	8.83%
1977-04	8.75%
1977-03	8.69%
1977-02	8.67%
1977-01	8.72%
1976-12	8.79%
1976-11	8.81%
1976-10	8.93%
1976-09	8.98%
1976-08	9.00%
1976-07	8.93%
1976-06	8.85%
1976-05	8.77%
1976-04	8.73%
1976-03	8.75%
1976-02	8.81%
1976-01	9.02%
1975-12	9.10%
1975-11	9.14%
1975-10	9.22%
1975-09	9.13%
1975-08	8.94%
1975-07	8.89%
1975-06	8.89%
1975-05	8.91%
1975-04	8.82%
1975-03	8.90%
1975-02	9.11%
1975-01	9.43%
1974-12	9.62%
1974-11	9.79%
1974-10	9.98%
1974-09	9.96%
1974-08	9.59%
1974-07	9.28%
1974-06	9.09%
1974-05	8.97%
1974-04	8.58%
1974-03	8.41%
1974-02	8.46%
1974-01	8.54%
1973-12	8.54%
1973-11	8.58%
1973-10	8.77%
1973-09	8.81%
1973-08	8.50%
1973-07	8.05%
1973-06	7.73%
1973-05	7.65%
1973-04	7.54%
1973-03	7.46%
1973-02	7.44%
1973-01	7.44%
1972-12	7.44%
1972-11	7.43%
1972-10	7.42%
1972-09	7.42%
1972-08	7.40%
1972-07	7.39%
1972-06	7.37%
1972-05	7.37%
1972-04	7.29%
1972-03	7.30%
1972-02	7.32%
1972-01	7.44%
1971-12	7.48%
1971-11	7.55%
1971-10	7.63%
1971-09	7.69%
1971-08	7.70%
1971-07	7.60%
1971-06	7.53%
1971-05	7.43%
1971-04	7.31%

Monthly chart values average the observations dated in each month; the latest month can be partial. These are current-vintage data. A historical rate applied to today’s price is a scenario, not a reconstruction of historical affordability.

Explore the city-wide rent history

Zillow Observed Rent Index, all rental homes and multifamily, smoothed and not seasonally adjusted. Missing months remain missing. A rent index divided by a sale-price median is not a matched-property rental yield.

Rent observation month

Rental benchmarks for Jul 2026
City	Monthly rental benchmark
San Bruno	$3,338
South San Francisco	$4,088

City-wide rents over time

Nominal dollars. Each series covers the named geography. 2019-01 to 2026-07.

San Bruno $3.3KSouth San Francisco $4.1K

View exact values (91 periods)

City-wide rents over time: nominal USD; missing values are unavailable
Month	San Bruno	South San Francisco
2026-07	$3,338	$4,088
2026-06	$3,312	$3,993
2026-05	$3,260	$3,933
2026-04	$3,173	$3,852
2026-03	$3,156	$3,774
2026-02	$3,128	$3,707
2026-01	$3,174	$3,650
2025-12	$3,167	$3,633
2025-11	$3,182	$3,610
2025-10	$3,123	$3,611
2025-09	$3,101	$3,598
2025-08	$3,090	$3,581
2025-07	$3,109	$3,625
2025-06	$3,096	$3,674
2025-05	$3,028	$3,676
2025-04	$3,001	$3,630
2025-03	$2,983	$3,553
2025-02	$3,003	$3,479
2025-01	$3,035	$3,480
2024-12	$3,021	$3,466
2024-11	$3,014	$3,520
2024-10	$2,977	$3,513
2024-09	$2,981	$3,533
2024-08	$2,966	$3,522
2024-07	$2,954	$3,514
2024-06	$2,939	$3,513
2024-05	$2,955	$3,504
2024-04	$2,953	$3,506
2024-03	$2,944	$3,497
2024-02	$2,917	$3,453
2024-01	$2,941	$3,403
2023-12	$2,947	$3,353
2023-11	$2,942	$3,359
2023-10	$2,873	$3,400
2023-09	$2,866	$3,432
2023-08	$2,872	$3,470
2023-07	$2,889	$3,473
2023-06	$2,873	$3,461
2023-05	$2,884	$3,429
2023-04	$2,896	$3,429
2023-03	$2,897	$3,434
2023-02	$2,893	$3,441
2023-01	$2,857	$3,427
2022-12	$2,854	$3,386
2022-11	$2,834	$3,391
2022-10	$2,872	$3,391
2022-09	$2,877	$3,435
2022-08	$2,893	$3,439
2022-07	$2,881	$3,430
2022-06	$2,840	$3,424
2022-05	$2,814	$3,391
2022-04	$2,803	$3,381
2022-03	$2,786	$3,348
2022-02	$2,757	$3,305
2022-01	$2,736	$3,283
2021-12	$2,734	$3,248
2021-11	$2,729	$3,234
2021-10	$2,700	$3,228
2021-09	$2,703	$3,248
2021-08	$2,692	$3,251
2021-07	$2,678	$3,175
2021-06	$2,662	$3,153
2021-05	$2,639	$3,122
2021-04	$2,595	$3,116
2021-03	$2,566	$3,048
2021-02	$2,569	$3,019
2021-01	$2,614	$3,036
2020-12	$2,661	$3,083
2020-11	$2,669	$3,131
2020-10	$2,709	$3,161
2020-09	$2,727	$3,182
2020-08	$2,772	$3,189
2020-07	$2,786	$3,242
2020-06	$2,814	$3,292
2020-05	$2,830	$3,346
2020-04	$2,834	$3,347
2020-03	$2,815	$3,355
2020-02	$2,821	$3,230
2020-01	$2,812	$3,207
2019-12	$2,826	$3,162
2019-11	$2,811	$3,251
2019-10	$2,848	$3,243
2019-09	$2,853	$3,243
2019-08	$2,862	$3,275
2019-07	$2,831	$3,220
2019-06	$2,827	$3,204
2019-05	$2,806	$3,171
2019-04	$2,785	$3,194
2019-03	$2,786	$3,182
2019-02	$2,783	$3,161
2019-01	$2,770	$3,148

Why rents and ownership costs belong in the lab

They make the price of a home easier to interpret. They also give us a separate longer-term hypothesis to test. Your cost assumptions do not become an appreciation forecast or a weight in the forecast mixer.

Research on prices and rents Research on ownership cost Data coverage and limits

Jobs & housing · structural evidence

Is housing keeping up with local growth?

See how employment and the number of homes changed. Housing-stock totals let us compare construction across cities of different sizes.

Supply comparison year

Housing: January 1. Jobs: Q2 snapshot. Completions: full calendar year.All housing types. Job history currently ends in 2023.

2023 snapshots and three-year growth through 2023. The jobs and stock reference months differ, as labeled.
Measure	San Bruno	South San Francisco
Total homes · Jan 1, 2023	16,732	23,083
Housing-stock growth · Jan 2020–Jan 2023	+0.7%	+1.8%
Workplace jobs · Q2 2023	14,821	59,458
Job growth · Q2 2020–Q2 2023	+18.3%	-5.5%
Completed homes during 2023	9	216
Completions per 100 homes present at the start of the year	0.05	0.94

Completions include replacement construction. “2 completions per 100 homes” does not mean the stock grew 2%. Workplace jobs are not resident households.

Housing stock over time

All series start at 100 in 2011. 2011 to 2023.

San Bruno 107.5South San Francisco 105.7

View exact values (13 periods)

Housing stock over time: homes; missing values are unavailable
January 1 snapshot	San Bruno	South San Francisco
2023	16,732	23,083
2022	16,696	22,877
2021	16,641	22,739
2020	16,619	22,677
2019	16,489	22,275
2018	16,430	22,211
2017	16,384	22,163
2016	16,327	22,131
2015	16,279	22,067
2014	15,907	22,031
2013	15,855	22,005
2012	15,784	21,861
2011	15,562	21,839

Workplace jobs over time

All series start at 100 in 2012. 2012 to 2023.

San Bruno 116.5South San Francisco 126.7

View exact values (12 periods)

Workplace jobs over time: jobs; missing values are unavailable
Q2 snapshot year	San Bruno	South San Francisco
2023	14,821	59,458
2022	14,415	59,026
2021	12,867	54,851
2020	12,533	62,940
2019	16,541	67,046
2018	16,589	67,061
2017	17,103	56,638
2016	16,447	54,847
2015	14,484	50,622
2014	12,309	49,063
2013	12,706	48,345
2012	12,718	46,919

The idea we’re testing

Stronger demand may translate into higher prices when housing responds slowly. A useful test needs both sides of that relationship. Low construction on its own can also reflect weak demand.

Evidence, gaps and the next test

Federal Reserve research on jobs and construction constraints supports studying the interaction. It does not establish a forecast for these cities.

The new stock history closes a denominator gap. The proposed multi-year forecasting study still has too few mature observations under its source-lag assumptions. Two completion years remain withheld: San Bruno 2020 and South San Francisco 2024. We need documented historical availability and broader coverage before a new predictive test.

DOF estimates include vacant homes and revisions. We use January snapshots consistently and reconcile the overlapping April 2020 benchmarks. Geography definitions across DOF, Census and city completion reports also need care. All charts are reconstructed from the retained releases.

Housing-stock sources Jobs and completion sources Download the evidence

Price forecasts · experimental

Explore a thesis, then check its record

The current annual forecasts use three price and tax-base signals. None earned selection over matching the county. The new Market outlook predicts contract time in days. It has its own weights and track record; cost scenarios and jobs/housing evidence also answer different questions.

See each family’s research and validation status →

Understand the models, then choose weights

These three models test price and tax-base signals. Each learns a relationship from earlier data, then estimates the city's next-year home-value growth relative to San Mateo County. An input is not itself a prediction.

None passed the original selection test. “Match the county” remains the selected baseline. The models share data and a study design; agreement is not independent confirmation.

Momentum · experimental

Recent price performance

Does how a city performed recently help predict its next year?

What it measures: The city's home-value growth over the past 12 months, minus San Mateo County's growth over those same months.
Made-up example · input only: City values rise 12% and county values rise 8%. The input is +4 percentage points of recent outperformance.
How to interpret it: The fitted relationship can favor a reversal. This model does not simply predict that recent winners keep winning; “Repeat recent relative growth” is the separate rule that does that.

Exact input calculation and earlier test

City 12-month percentage change − county 12-month percentage change.

Average absolute prediction error in the earlier model-selection test: 1.601 percentage points. County baseline: 1.346. Lower is better; selection also required consistency across years.

Momentum weight (%)

Relative value · experimental

Price gap versus history

Does a change in a city's usual price relationship to the county help predict its next year?

What it measures: Today's city-to-county home-value ratio, compared with its average over the preceding 60 months.
Made-up example · input only: The city's index is 80% of the county index today, versus an average of 88% previously. The model's gap input is +10%: 88 ÷ 80 − 1.
How to interpret it: This measures relative price level, not relative growth or affordability. A positive gap is not proof of a bargain and does not imply a 10% price rise.

Exact input calculation and earlier test

100 × (average of the preceding 60 monthly city/county ratios ÷ current ratio − 1). The current month is excluded from the average.

Average absolute prediction error in the earlier model-selection test: 1.398 percentage points. County baseline: 1.346. Lower is better; selection also required consistency across years.

Relative value weight (%)

Assessment growth · experimental

Taxable property growth

Does growth in the taxable property base help predict home-value growth?

What it measures: Three-year growth in the city's net assessment roll, minus the county's growth. The roll is the taxable value of property, across property classes.
Made-up example · input only: The city's taxable property total grows 12% over three years and the county's grows 8%. The input is +4 percentage points.
How to interpret it: Tax rules and multiple property classes affect this total. It is not a home-price index, city revenue, or a direct measure of jobs or commercial growth.

Exact input calculation and earlier test

City three-year net assessment-roll percentage change − county change. Use January of the year before the July feature cutoff, with a three-year lookback. This assumed lag is not verified historical publication timing.

Average absolute prediction error in the earlier model-selection test: 1.392 percentage points. County baseline: 1.346. Lower is better; selection also required consistency across years.

Assessment growth weight (%)

Weights total 0%. Set whole percentages totaling 100% to see a blend.

A 50% weight means half of that model's prediction contributes to the mix. It does not mean a 50% chance the model is right. Zero removes a model; changing weights does not retrain it. The exact equal-weight average stays visible as a comparison.

Choose a single model or set weights totaling 100%.

Methods, versions and source files

Component forecasts were issued 2026-09-15T02:02:10Z. model-lab-v1 uses the original housing-screen-v1 rules and data. Every model shares the same target, benchmark, home type, cutoff and horizon. A missing selected prediction withholds the blend; no weight is silently reassigned.

Earlier validation selected the county baseline. The original decision and paper record stay unchanged. Scores here are recomputed from blended predictions, not averaged component errors.

Download all component predictions, historical outcomes and source hashes · Read the original track record

Source manifest SHA-256: 46f922fcefd73314d74781232e4679632900b1fe429ec6af31c61ab00df388d7

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