April Housing Intelligence Report: Methodology

April Housing Intelligence Report: Methodology

The April Housing Intelligence Report features proprietary Parcl Labs metrics derived from our comprehensive real estate data ecosystem. Below, we outline the methodology behind each metric and insight, including specific calculations and data sources. All data referenced is accessible through the Parcl Labs API.

Realtime Market Conditions

Price Growth YoY: To calculate price growth, we use our real-time price feed and compare the current reading with the same reading a year ago. For example, if we are looking at the prices for Austin City on March 20, 2025, we compare that reading with the value from our index for March 20, 2024.

Year to Date Price Growth: To calculate price growth, we use our real-time price feed and compare the current reading with the same reading from January 1 of the corresponding year. For example, if we are looking at the prices for Austin City on March 20, 2025, we compare that reading with the value from our index for January 1, 2025.

Peak Value to Current: The peak value to current is calculated by identifying the date when our real-time price feed was at its highest point and then comparing that with the current reading as a percentage of the maximum value. For example, if the highest value for Seattle metro was registered on June 22, 2022, we would compare that to the date we are analyzing (e.g., March 20, 2025). It is worth noting that all of our price feeds go back to January 1, 2020

Change since COVID: To make this comparison, we take the value of the market of interest on February 1, 2020, and compare that number with the current reading as a percentage of the start-of-COVID value.

Supply: We gather all inventory available for sale in a given market from September 2022 to the most recent weekly data point. To compare available inventory to the overall number of sales, we convert this metric to a monthly basis by taking the median value for a given month and parcl, and use this as an input for other metrics such as absorption rates and supply and demand data. The data is refreshed on a weekly basis and is provided for all property types in our system.

Demand: We define demand as the total number of properties sold on a monthly basis for a given parcl. This metric is combined with supply to generate metrics such as absorption rate and demand and supply gaps. The history of total sales in the endpoint used for this data point goes back to January of 2019. The data is refreshed on a monthly basis and is provided for all property types in our system.

Supply and Demand Gap: To calculate the market gap, we first obtain the year-over-year change in both supply and demand for a given market up to the most recent available monthly data. After that is calculated, we compute a three-month moving average of changes in both supply and demand, which provides a more reliable way of tracking changes in sales and inventory. Finally, we subtract the three-month moving average of supply change from the three-month moving average of demand change to arrive at a final number. Throughout the report, this gap represents the difference between total available inventory and total sales, and in charts it is displayed as the market gap.

Surplus: Surplus is defined as the available inventory for sale in a given month minus the total sales in that month. Throughout the report, we present a year-over-year comparison to provide a complete snapshot of the real estate market for a given parcl.

Absorption Rate: This is calculated as the number of properties sold in a given month divided by the overall number of properties available for sale, expressed as a percentage (i.e., the percentage of inventory purchased). Throughout the report, we present a year-over-year comparison to provide a complete snapshot of the real estate market.

Market Correction Definitions: We use standard definitions to consistently classify real estate market conditions based on price movements. These definitions provide clarity when understanding and interpreting price declines across different markets:

• Bear Market: A market is in a bear market if prices have declined by 20% or more from their peak.
• Correction Territory:
  • A market is in correction territory if prices have declined between 10% and 20% from their peak.
  • A market is approaching correction territory if prices have declined between 5% and 10% from their peak.

Sub-Market Analysis

For submarket-level analysis, we present metrics at the ZIP code and county levels by focusing on the top 10 geographies within a market, based on the number of homes. This approach is designed to highlight the most active and relevant areas while maintaining a manageable scope for comparison. It is not intended to be fully comprehensive but rather to surface meaningful insights from the most representative submarkets.

SFH Sale: This section shows the total number of Single-Family Homes (SFH) available for sale (supply), total homes sold (demand), and the resulting supply-demand gap (defined previously). Metrics are provided for both county and ZIP code levels and reflect data for the most recent available month (January 2025 in this analysis).

SFR Market: Displays the gross yield for single-family rental properties (defined previously), along with the median rental price for single-family homes. Data are presented for both county and ZIP code levels, based on the most recent month available (February 2025).

Our report includes investor-specific metrics to analyze real estate market dynamics within individual submarkets. Below, we define each investor-focused metric, detailing calculation methods aligned precisely with categories outlined in our Investor Data Documentation.

Investor: Includes investor-specific metrics at the county and ZIP code levels, reflecting the latest available data (February 2025):

• Large Institutional Share: The percentage of single-family homes owned by investors with portfolios of 1,000+ properties, indicating the extent of large institutional presence.
• All Investor Market Share: The total percentage of single-family homes owned by investors across all portfolio sizes.
• Purchase-to-Sale Ratio: Calculated as the number of properties acquired by investors divided by the number of properties sold by investors, highlighting investor activity within each market.
• Gross Yield:  is calculated by dividing the annual median rental income—derived from multiplying the monthly median new rental listing price by 12—by the median new listing sale price. This method ensures that yield calculations reflect both current market conditions and historical trends.

Investment Opportunity Analysis

Investor Categories: Investor data in our analysis is segmented into three primary groups to capture varying scales and types of investment activities:

• Overall Investor Cohort: General trends and activities across all investor types.
• Single-family Portfolio Owners: Segmented by portfolio size:
  • 2-9 Properties: Small-scale investors typically involving individuals or small entities.
  • 10-99 Properties: Medium-scale investors managing multiple properties.
  • 100-999 Properties: Mid-sized investors owning significant portfolios.
  • 1000+ Properties: Institutional investors and large-scale REITs.
• Major Institutional Owners: High-level corporate entities (e.g., Invitation Homes, American Homes 4 Rent, Progress Residential) identified using advanced methodologies to map LLCs and subsidiary structures back to their controlling corporate entities.

Clusters of Properties: We identify clusters of real properties using K-means clustering with a sophisticated metrics-based approach. Our methodology preprocesses property data by imputing missing values and applying standardized scaling, with configurable weighting between geographic and non-geographic features. The optimal number of clusters is determined through a composite evaluation framework that balances silhouette scores (measuring cluster separation and cohesion), inertia (quantifying cluster compactness), and size ratios (ensuring balanced cluster distributions). This evaluation incorporates a second-derivative analysis to identify significant inflection points in the inertia curve. Once clusters are established, comprehensive statistical profiles are generated for each segment, capturing central tendencies in prices, yield rates square footage, year built, etc. with each cluster quantified as a percentage of the overall property inventory to facilitate strategic market analysis.

List of Figures

Figure 1 Home Price Performance: YoY Comparison (2020-2025): This figure compares home price performance starting from January 1 of each year (2020–2025), enabling a direct analysis of seasonal patterns and market momentum across different annual cycles.

Figure 2 Rolling Count of New Listings: This metric tracks weekly counts of new property listings using a 30-day rolling window, providing a reliable measure of newly listed properties within each market. For the annual comparison, we compare the current 30-day rolling count against the same period from the previous year to highlight year-over-year trends.

Figure 3 Rolling Count of Price Cuts: This metric captures the weekly number of price reductions for listed properties using a 4-week rolling window to provide a consistent estimate of price-cut activity. For year-over-year analysis, we compare the current 4-week rolling count of price reductions against the equivalent 4-week period from the previous year, allowing for a direct assessment of trends in pricing adjustments.

Figure 4 Monthly Counts of Inventory and Sales with Absorption rate: This chart displays the monthly counts of inventory (supply) and completed transactions (demand) for all property types. The absorption rate, shown in the hover menu, represents the percentage of available inventory sold within a given month. Conversely, surplus represents the portion of inventory remaining unsold at month-end. Data presented covers the period from September 2022 to January 2025.

Figure 5 Supply and Demand Gap of a given Parcl compared to the US: This chart illustrates the supply and demand gap (as previously defined) for a specific parcl compared to the national average for the U.S. It also highlights the difference between the local market gap and the national average, indicating whether the local market is experiencing greater or lesser distress relative to the overall U.S. housing market. Data presented covers the period from November 2023 to January 2025.

Figure 6 Home Market Metrics for SubMarket Analysis: This figure presents metrics that track different aspects of local markets, Zip Codes and Counties, for different metrics such as SFH Sale, SFR Markets and Investor Metrics. The definition of the specific metrics used in each subsection can be found above. Data coverage for Figure 6 is February 2025 for both SFR Market and Investor metrics, and January 2025 for SFH Sale. The YoY calculation represents the percent change from the same month one year prior.

Figure 7: Property Clusters Based on Market Segmentation: This chart presents distinct clusters of properties ("buy boxes") identified through the clustering methodology detailed above. Each cluster groups properties with similar characteristics, including size, age, location, and property type, to facilitate targeted market analysis. For each cluster, we present key metrics: gross yield, monthly rent, list price, sale price, square footage, bedroom and bathroom counts, typical year built, active listings, active rentals, and recent sales. The accompanying visualizations illustrate trends in gross yield, price changes, and supply-demand gaps, enabling effective benchmarking across identified segments.