Start with the property
The address, units, price, rent assumptions, financing, operating costs, and risk context define the property’s starting point.
Platform · Methodology
PriceDNA Methodology.
How PriceDNA turns property inputs, local data, and your investment goals into a clearer real estate analysis.
Methodology
The flow is simple.
PriceDNA combines the facts of a property with local context and an investor’s goal. The result is not just a market estimate; it is a personalized analysis of whether the property and the goal can meet.
Add local context
PriceDNA brings in neighborhood and market signals so the analysis is not floating above the actual place where the property sits.
Work from the goal
Instead of only asking what the market says, PriceDNA asks what price, assumptions, and outcomes would fit the investor’s target.
The defining method
Start with the goal. Work back to the price.
PriceDNA asks a question that is personal to you as an investor: what purchase price can your investing goal support, given the property’s income, expenses, and loan terms? Choose a goal such as cash-on-cash return, debt service coverage ratio, or net cash flow per unit, and PriceDNA works backward through the deal’s assumptions to the price that meets it. That result is the PriceDNA Estimate™, and the method is called goal-convergent valuation.
Two investors can reach different supported prices for the same property, because their goals and financing differ. A supported price is not a forecast of what a seller will accept.
The PriceDNA Location Score™ is a separate signal describing the location rather than the deal. It does not enter the goal-convergent calculation and does not move the Estimate.
Do age, condition, and size count?
Yes, through the rent and costs you enter or confirm. PriceDNA does not have a field for age, condition, square footage, or lot size, and does not add or subtract a set amount for each one. Those features show up where they affect the deal: achievable rent, repairs, make-ready, insurance, maintenance, and taxes. Number of units is a direct input, because it changes the unit-level math.
Say the seller updated the kitchen, but rent is still $900 a month. If similar rentals show that $975 is achievable, you can model the extra $75 as upside. PriceDNA accounts for the update through that achievable rent, not through a separate premium for the finishes.
That is a different question from what the physical characteristics are worth on the open market. The IRS valuation guidelines describe three conventional approaches, including a sales comparison that adjusts for physical differences and an income approach that works from rent, expenses, and vacancy. PriceDNA solves for a price under your goal and your assumptions. That purpose is not appraisal, and PriceDNA is not an appraisal.
Local area
How local is “local”?
A census tract in a dense city can hold a few thousand people. A rural tract can cover hundreds of square miles. Statistics drawn from a single tract are often too noisy to rely on, so PriceDNA builds a local area from the property’s census tract and nearby tracts, until there are enough people behind the numbers for the estimates to hold up. Dense areas resolve to a small radius, sparse areas to a larger one.
Two established geographies bracket the scale PriceDNA works at. Research on Chicago neighborhoods grouped census tracts into clusters of roughly 8,000 people, chosen to stay small enough to approximate a local neighborhood. New York City’s Neighborhood Tabulation Areas use a 15,000-person minimum, set because population size affects the error in the population projections they were built for. PriceDNA works toward about 12,000. Neither geography sets that number; they show the scale others have found workable.
Twelve thousand residents is a target for how much data stands behind a number, not a promise of precision. The survey reaches only a sample of them.
Grouping data this way is not unusual. FHFA builds “supertracts” for its own tract-level price indices, combining adjacent tracts until there are enough transactions to estimate an index.
Two details are worth stating plainly. The area is built outward from the property’s census tract, not measured from the property’s exact coordinates, so it locates the neighborhood rather than a precise distance. And because whole tracts are included, the reported radius can reach beyond the distance the search itself covers. In sparse places the area may widen, or a measure may fall back to county context, which the report labels where it happens.
Data granularity
When local data runs out.
Many signals are available at the census-tract level. Others are only published or statistically reliable at broader geographies such as county, metro, state, or national level.
When tract-level data is not available or would be misleading, PriceDNA uses the best available broader geography or a documented fallback method instead of pretending the data is more precise than it is.
Depending on the metric, that fallback may be a county value, metro value, state benchmark, national benchmark, or a modeled estimate based on nearby comparable geographies.
This is why some radius-based metrics may show the same value across the 1-mile, 3-mile, and 5-mile views. That usually means the most reliable available source for that metric is broader than the radius itself, or that the nearby comparison area does not contain enough distinct data to support separate values at each distance.
In those cases, PriceDNA keeps the value consistent rather than inventing false precision. The repeated number is a signal about the data’s available resolution, not a calculation error.
Interpretation
Markets are not uniform, so PriceDNA shows distributions.
A single average can hide the part of a city that matters most. PriceDNA often shows percentile ranges, medians, and local distributions because the spread can be more useful than one citywide number.
That is especially important for investors comparing neighborhoods inside the same market. The question is rarely “What is the city average?” The better question is “Where does this property sit inside the local range?”
Projected growth
How PriceDNA projects rent and price growth.
Rent and price growth follow the local long-run compound annual growth rate (CAGR), and never exceed it. PriceDNA applies the same method to two sources: the FHFA House Price Index for prices and HUD Small Area Fair Market Rents for rents.
For each local area, the calculation starts with two compound annual growth rates: a long-run rate measured over approximately the past decade, and a recent rate measured over the past three years. Each rate is annualized over the actual number of years in its window. The projection leans on the recent rate in the early years, so a cooling market shows through quickly, then settles toward the long-run rate over a longer hold. It is capped at the long-run rate throughout.
House prices tend to run in the same direction over a year or two, then revert toward longer-run trends over longer horizons, and how strongly they do so varies by market. Research on local house price accelerations finds that rapid run-ups can overshoot sustainable price levels, and that what follows differs from place to place. Work on real house price dynamics across 62 metro areas finds the same pattern, with the strength of both effects varying by location. PriceDNA caps projected growth at the local long-run rate for that reason, so a recent surge cannot lift the projection above what the area has sustained over time.
That research motivates the cap. It does not set the windows, the weighting, or the decision to apply the same construction to rents. Those are PriceDNA’s choices. Both studies also measure real, inflation-adjusted prices, while these projections are in nominal terms, because the FHFA index is published without an inflation adjustment.
The cap is applied uniformly. It limits the effect of recent acceleration, but it does not make the projection a floor. It can understate sustained growth, and it can overstate growth in a market that keeps weakening.
Geography
How PriceDNA handles city and tract assignment.
City-level market pages are built from census tracts, but real-world boundaries do not always line up cleanly. PriceDNA uses consistent geographic rules so a tract is not double-counted across multiple cities.
Tract-to-city assignment
For city-level market pages, PriceDNA assigns a census tract to a city when the tract’s geographic centroid falls within that city’s boundary. This keeps each tract assigned to one city, even when a tract touches or crosses multiple municipal boundaries. It also avoids double-counting when PriceDNA summarizes city-level distributions.
Related methods
Two systems make the methodology more personal and more local.
Data sources
Public data, organized for investment context.
PriceDNA uses federal, public, and research-backed datasets to describe local conditions. The goal is not to overwhelm the user with raw data. The goal is to translate the right signals into a calmer investment view.
Research
What the methods draw on.
These studies inform choices described above. None of them validates PriceDNA’s outputs, and none was written about PriceDNA.
Bogin, Doerner and Larson (2016), Local House Price Growth Accelerations. Rapid local run-ups can overshoot sustainable price levels, and what follows differs by place. Supports capping a projection, not the windows or weighting PriceDNA uses.
Capozza, Hendershott, Mack and Mayer (2002), Determinants of Real House Price Dynamics. Prices show short-horizon momentum and longer-horizon reversion, and both vary by market. Measures real prices; PriceDNA’s projections are nominal.
Sampson, Raudenbush and Earls (1997). Grouped Chicago census tracts into clusters of about 8,000 people. Cited for the scale of a workable neighborhood, not as a statistical threshold.
FHFA tract-level house price index FAQ. Describes supertracts, and states that the published appreciation rates are nominal.
What PriceDNA does not claim.
PriceDNA is not an appraisal, legal opinion, tax opinion, insurance determination, government map, or financial advice. It is a decision-support platform designed to help investors understand a property through their own numbers and local context. Users should review important decisions with their own qualified professionals, including financial advisors, real estate agents, insurance agents, tax professionals, attorneys, or other appropriate experts.