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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsTo find out whether home prices are rising where you live, track one consistently defined local price or value series over time. Keep the geography, property type, measurement method and seasonal-adjustment status the same, then compare the latest observation with the same period a year earlier and with a longer baseline. A repeat-sales index is usually the cleanest measure of like-for-like price change; an estimated-value index covers more homes but is model-based. Median sale price is useful context, not a verdict, because the mix of homes sold can change.
No single index can answer the question for every neighborhood. Your conclusion should identify the named series, geography, housing type, dates and percentage change, and should acknowledge when local data are thin, delayed or methodologically different.
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Start by defining exactly which market you mean
“My local market” could mean a metropolitan area, county, city, ZIP code, subdivision or a particular type of home. Those are not interchangeable. A metro-wide increase can coexist with falling prices in one neighborhood, while a ZIP-code series may be too noisy to describe a single street.
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Choose the geography
Pick the smallest geography with a credible, sufficiently complete time series that matches your decision. Federal Housing Finance Agency (FHFA) data are available at national, state, metropolitan, county, ZIP-code and census-tract levels through its House Price Index program. Zillow also publishes value series by geography through its Housing Data portal.
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- Metro or county: generally more observations and smoother trends, but less specific to your neighborhood.
- City or ZIP code: more locally relevant, with greater risk of missing data, revisions and month-to-month noise.
- Census tract or smaller area: potentially useful for a narrowly defined question, but only if the series has adequate coverage.
Write down the geography exactly as the provider labels it. Do not compare a city series with a county series and call the difference a neighborhood trend.
Match the housing type
Use the same property category in every period: for example, single-family homes, condos, all residential properties or a specified price tier. A local market can show rising prices for detached houses while condominium values are flat. If one source covers only eligible single-family transactions and another estimates values for all home types, their rates are not directly interchangeable.
Set the time window
For a current read, record the latest available month or quarter, compare it with the same month or quarter one year earlier, and examine a multi-year baseline. Year-over-year comparisons reduce the chance that normal seasonal buying patterns will be mistaken for appreciation. A three- to five-year view can show whether a recent change is part of a sustained trend or a short-lived reversal.
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Always state the exact dates. “Prices are up recently” is not reproducible; “the county single-family index rose from the second quarter of 2025 to the second quarter of 2026” is.
Know what each measure actually measures
Different indicators answer different questions. Compare percentage changes within a consistently defined series, not the numerical level of one provider’s index with another provider’s level.
| Measure | Best question it answers | Important limitation |
|---|---|---|
| Repeat-sales index, such as FHFA HPI or Redfin RHPI | How prices for properties observed in multiple transactions changed over time | Coverage is limited to the transactions and properties eligible for that provider’s method; it is not every home. |
| Estimated typical-value index, such as Zillow ZHVI | How modeled typical values changed across a defined geography and housing type | Values are estimates from a model, not recorded prices for every completed sale. |
| Median sale price | What the middle observed sale price was during a period | The median can move because the types, sizes or locations of homes sold changed. |
| Local closed-sale records or MLS summaries | What actually sold in the selected market | Small samples, reporting delays, concessions and property mix can distort a short period. |
Use a repeat-sales index for the clearest like-for-like signal
A repeat-sales index compares transactions involving the same properties at different times. Because it follows repeat observations of a property rather than simply averaging whatever sold in a month, it is less exposed to the composition effect that can move a median.
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What FHFA HPI covers
FHFA’s HPI uses weighted repeat-sales methodology. Depending on the variant, it includes eligible single-family home purchase transactions and refinancings associated with mortgages purchased or securitized by Fannie Mae or Freddie Mac. FHFA says its index suite is constructed from more than 100 million home sale and refinance transactions. Its overview covers all 50 states and more than 400 American cities, with additional county, ZIP-code and census-tract datasets. Those are the scope of the index program, not a count of current sales in your locality.
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Why the method matters
FHFA explains the distinction this way: “Those measures reflect changes in both the prices and the composition or quantity of housing, whereas the FHFA HPI is designed to isolate changes in house prices.” The statement refers to measures such as aggregate or median prices that can change when the homes sold change. A repeat-sales index does not eliminate every source of uncertainty, but it is designed to focus more directly on price movement for comparable properties.
Limits of FHFA data
- Only transactions meeting the index’s mortgage and property eligibility rules enter the relevant series.
- Very recent observations can lag the date a contract was signed or a deed was recorded.
- Small-area series can be less complete and more volatile than national or state series.
- Different FHFA variants have different transaction populations; do not switch variants mid-comparison.
- Values and historical observations can be revised as additional records arrive.
Use an estimated-value index to broaden coverage
Zillow’s Home Value Index (ZHVI) measures typical home values using Zillow’s property-value estimates. The Zillow Research data portal provides series by geography and housing type, including smoothed, seasonally adjusted and raw versions.
When ZHVI is useful
An estimated-value series can cover a broader set of homes than a transaction-only index and can provide a timely view where repeat-sale records are sparse. Select the housing type and geography that match your question, then keep the same ZHVI series for every date in the calculation.
How to read the estimate
ZHVI is a modeled estimate of typical value, not an appraisal or a ledger of every closing. The model can respond differently from a repeat-sales index because the data population, smoothing and estimation process differ. Use its percentage change to understand the direction of that series, not to claim that every individual home gained the same amount.
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Do not treat median sale price as the verdict
The median is the middle sale price in the observed set. It is easy to obtain from an MLS report or public-record summary, but it does not hold the homes constant. If more high-priced houses sell this month, the median can rise even when comparable homes have barely changed. If more entry-level properties sell, the median can fall without broad depreciation.
Median data are still valuable. Pair them with a repeat-sales or estimated-value index and inspect the number of sales, property types, price tiers and locations included. Redfin’s methodology discusses composition effects and its repeat-sales index; it also documents seasonal adjustment. The point is not to discard the median, but to avoid converting a changing sales mix into a claim about every home.
A practical workflow for answering “Are prices going up here?”
- Write the market definition. Record the geography, housing type and whether you are studying prices, estimated values or completed sales.
- Choose a primary series. For a like-for-like signal, start with a suitable FHFA repeat-sales series. If you need broader modeled coverage, use a defined ZHVI series. Confirm that the series exists for your geography and property type.
- Capture the metadata. Note the series name, provider, latest observation date, comparison dates, seasonal-adjustment status, data population and any stated lag or revision policy.
- Download or record matching observations. Use the same series and units for the latest period and the prior-year and multi-year dates. Do not mix a seasonally adjusted observation with an unadjusted one.
- Calculate the percentage change. Use
(latest index ÷ comparison-period index − 1) × 100. For a one-year rate, use the same month or quarter one year apart. - Check a second indicator. Compare the direction with another credible index or with local closed-sale or MLS summaries. Keep the comparison narrow: same geography, housing type and period wherever possible.
- Inspect the context. Look at sales volume, property mix, recording delays, concessions and whether the latest point is preliminary or revised.
- Write a qualified result. Identify the series and say exactly what changed: “The [named index] for [geography and housing type] rose/fell [rate] from [date] to [date].”
The calculation describes the selected series. It is not a precise estimate of what one particular house would sell for today.
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Handle seasonality and short-term noise correctly
Housing activity follows strong seasonal patterns. Spring and summer often have different transaction volumes and property mixes from winter, so a month-to-month move can be misleading. For short-term comparisons, prefer a seasonally adjusted series when one is available and state that adjustment in your notes.
FHFA identifies its flagship purchase-only index as seasonally adjusted. Redfin’s methodology describes seasonal adjustment for its repeat-sales index, and its housing tracker notes that monthly metrics are seasonally adjusted except median sale price. Zillow offers smoothed, seasonally adjusted and raw versions of ZHVI. Do not compare a raw January value with a seasonally adjusted February value and attribute the entire difference to market appreciation.
When a month-to-month change is still useful
A monthly move can flag a possible turning point, especially when several consecutive observations point in the same direction. Treat one small move as provisional if the area has few sales, delayed records or frequent revisions. Confirm it with a year-over-year comparison and a longer baseline.
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Cross-check indicators without mixing their meanings
Agreement across measures strengthens confidence in the direction, but disagreement is not automatically an error. It often reflects differences in coverage and method.
If the repeat-sales index rises but the median falls
More lower-priced homes may have sold, pulling the median down while like-for-like prices increased. Check the number of sales and the distribution by property type and price tier.
If the median rises but the repeat-sales index is flat
A larger share of expensive homes may have sold. The median describes that period’s sales mix; the repeat-sales index is designed to isolate price change among repeat-observed properties.
If FHFA and Zillow point in different directions
First verify that you selected the same geography, housing type and dates. Then compare each provider’s data population and adjustment status. FHFA’s eligible mortgage-linked transactions and Zillow’s modeled typical values do not cover the same homes, so their rates need not match. Report the scope of each result instead of selecting whichever number is more dramatic.
If local records conflict with an index
Check whether the local report uses contract dates, closing dates or deed-recording dates; whether concessions are included; and whether the sample is large enough. A local MLS summary may be closer to current transactions but can be noisier. An index may be smoother or lagged. Use a matched segment and adequate period before drawing a conclusion.
Adjust for inflation when the question is purchasing power
A nominal index shows movement in house prices measured in dollars. If you want the change after general inflation, FHFA says you can adjust the HPI with an appropriate consumer-price inflation measure. State the inflation series and the dates used. An inflation-adjusted result answers a different question from the nominal change: whether home prices rose faster or slower than the general price level.
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Do not subtract an unspecified inflation rate from a local home-price percentage. Use a clearly named consumer-price measure covering the same period, and explain whether the result is a nominal or real change.
Understand data lags, revisions and uncertainty
- Recording lag: deeds and mortgage records may appear after the transaction was agreed or completed.
- Small samples: a few sales can create a large median move or a volatile small-area index.
- Property mix: changes in size, condition, location and type can move sale-price statistics.
- Coverage rules: an index may exclude cash purchases, certain loans, multifamily properties or other transactions, depending on its definition.
- Model uncertainty: estimated values depend on the provider’s valuation model and available data.
- Revisions: historical values can change as late records are incorporated or methodology is updated.
- Geographic mismatch: a county or metro trend may not represent your neighborhood.
These limits do not make the data useless. They determine how narrowly you should phrase the conclusion and how much weight to place on a recent observation.
Compare markets fairly
If you are comparing two neighborhoods, counties or indicators, hold the comparison axes constant:
- the same geography definition or an explicitly equivalent one;
- the same housing type and price tier;
- the same start and end periods;
- the same seasonal-adjustment status;
- the same kind of measure, such as repeat-sales index versus repeat-sales index;
- the same data-population description and treatment of revisions.
Compare percentage changes within each provider’s series. Do not compare the level of an FHFA index directly with the level of a Zillow index; their scales and definitions are different.
How to report your conclusion responsibly
A useful local-market statement contains five pieces:
- the named provider and series;
- the geography;
- the housing type;
- the exact periods and whether the data are seasonally adjusted;
- the calculated percentage change and a brief limitation if the sample is narrow or delayed.
For example, use this structure: “The [provider and series] for [defined geography] and [housing type] changed [up or down] by [calculated rate] from [comparison date] to [latest date]. The series is [seasonally adjusted or not] and covers [provider’s stated population]. A second measure [agrees or differs], likely because [documented coverage or composition difference].” Replace every bracketed element with the actual published label and value; do not turn the result into an estimate for an individual property.
What the available data can—and cannot—tell you
FHFA’s index program provides unusually broad geographic coverage and a long repeat-sales record, while Zillow’s value series can offer modeled coverage across many geographies and housing types. Local MLS or recorder data show actual sales but require care with timing and composition. Used together, these sources can answer whether a defined local segment is rising, falling or roughly flat.
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