View quarterly updates and historical trends for Puerto Rico House Price Index. Source: Federal Housing Finance Agency. Track economic data with YCharts a…

Housing Index in Puerto Rico increased to 246.52 points in the second quarter of 2025 from 238.16 points in the first quarter of 2025. This dataset provides - Puerto Rico House Price Index- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Puerto Rico Home Mortgage Finance Market Is Segmented By Application (Home Purchase, Refinance, Home Improvement, And Other Applications), By Provider (Banks, Housing Finance Companies, And Real Estate Agents), And By Interest Rate (Fixed Rate Mortgage Loan And Adjustable Rate Mortgage Loan). The Report Offers Market Size And Forecasts For The Puerto Rico Home Mortgage Finance Market In Value (USD) For All The Above Segments.

The Puerto Rico home mortgage finance market, while smaller than major global markets, exhibits significant growth potential driven by factors such as increasing population, government initiatives to boost homeownership, and fluctuating interest rates. The market's compound annual growth rate (CAGR) exceeding 1.50% suggests a steady, albeit moderate, expansion. Key market segments include home purchases, refinancing, and home improvements, with banks and housing finance companies acting as primary providers. Fixed-rate mortgages dominate the market, although adjustable-rate mortgages also play a role. While precise market size data for Puerto Rico is unavailable, a reasonable estimation, considering comparable Caribbean markets and the provided CAGR, would place the 2025 market size in the range of $500-700 million USD. This estimate incorporates the influence of factors such as economic conditions, government policies, and lending practices specific to Puerto Rico. Further growth is anticipated through 2033, driven by sustained demand for housing and continued government support. However, potential restraints such as economic instability, fluctuating interest rates, and stringent lending criteria could impact growth trajectory. The market's competitive landscape includes both local and international players, highlighting the presence of established institutions like Banco Popular and Scotia Bank alongside smaller credit unions. The geographic focus is primarily Puerto Rico itself. However, considering the data provided, the market analysis implicitly considers external influences, such as global interest rate trends and investment from larger financial institutions with operations in the region, influencing investment and loan availability. The provided list of companies indicates a presence of both national and international players further demonstrating the market’s dynamic and mixed nature. Future growth will likely depend on successfully navigating economic challenges and maintaining a stable and supportive regulatory environment. A focus on technological advancements within the mortgage application process and enhanced financial literacy programs may further enhance market expansion. Recent developments include: February 2023: Puerto Rico was expected to receive up to USD 109 million in funding under the State Small Business Credit Initiative (SSBCI) in President Biden's American Rescue Plan. The Treasury has now said that state and territory plans totaling over USD 6 billion in SSBCI funding have been approved. This is to help small businesses and entrepreneurs and make it easier to get access to capital., February 2023: The Consumer Financial Protection Bureau (CFPB) permanently banned RMK Financial Corporation, which does business as Majestic Home Loans, from the mortgage lending industry by prohibiting RMK from engaging in any mortgage lending activities or receiving remuneration from mortgage lending in Puerto Rico.. Notable trends are: Increase in Economic Growth and GDP per capita.

Graph and download economic data for All Employees: Financial Activities: Real Estate and Rental and Leasing in Puerto Rico (SMS72000005553000001) from Jan 1990 to Aug 2025 about Puerto Rico, leases, real estate, rent, financial, employment, and USA.

In 2022, San Jose, CA, was the hottest market for millennial homebuyers in the United States. Millennials in San Jose were responsible for nearly ** percent of the house purchase requests. Denver, CO, and Boston, MA, completed the top three with over ** percent of purchase requests. Which are the states with the youngest population in the U.S.? It should come as no surprise that the demographic composition plays a central role in the development of the housing market in different states. In 2020, the median age in the United States was 38.2 years, but some states, such as Alaska, District of Columbia, and Utah had much younger population. In contrast, Maine, Puerto Rico, and Hampshire had the highest median age of population. Millennials’ attitudes towards homeownership While many millennials have given up on homeownership, one in ***** people share that they are in the process of saving for a home purchase. These results suggest that young Americans have not entirely given up on the American dream of owning a home of their own.

2025 HUD Fair Market Rents (FMR) for Section 8 Housing Choice Voucher Program in Puerto Rico. Includes rent limits by city and county for studio through 4-bedroom units.

Graph and download economic data for All Employees: Financial Activities: Real Estate and Rental and Leasing in San Juan-Bayamon-Caguas, PR (MSA) (SMU72419805553000001SA) from Jan 2003 to Aug 2025 about San Juan, Puerto Rico, leases, real estate, rent, and employment.

Graph and download economic data for All Employees: Real Estate and Rental and Leasing in San Juan-Carolina-Caguas, PR (MSA) (SMU72419805553000001A) from 2003 to 2024 about San Juan, Puerto Rico, leases, real estate, rent, and employment.

The property level flood risk statistics generated by the First Street Foundation Flood Model Version 2.0 come in CSV format.

The data that is included in the CSV includes:

• An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.

• The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.

• The property’s Flood Factor as well as data on economic loss.

• The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms this year and in 30 years.

• Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for this year and in 30 years.

• Information on historical events and flood adaptation, such as ID and name.

This dataset includes First Street's aggregated flood risk summary statistics. The data is available in CSV format and is aggregated at the congressional district, county, and zip code level. The data allows you to compare FSF data with FEMA data. You can also view aggregated flood risk statistics for various modeled return periods (5-, 100-, and 500-year) and see how risk changes due to climate change (compare FSF 2020 and 2050 data). There are various Flood Factor risk score aggregations available including the average risk score for all properties (flood factor risk scores 1-10) and the average risk score for properties with risk (i.e. flood factor risk scores of 2 or greater). This is version 2.0 of the data and it covers the 50 United States and Puerto Rico. There will be updated versions to follow.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

The data dictionary for the parcel-level data is below.

Field Name	Type	Description
fsid	int	First Street ID (FSID) is a unique identifier assigned to each location
long	float	Longitude
lat	float	Latitude
zcta	int	ZIP code tabulation area as provided by the US Census Bureau
blkgrp_fips	int	US Census Block Group FIPS Code
tract_fips	int	US Census Tract FIPS Code
county_fips	int	County FIPS Code
cd_fips	int	Congressional District FIPS Code for the 116th Congress
state_fips	int	State FIPS Code
floodfactor	int	The property's Flood Factor, a numeric integer from 1-10 (where 1 = minimal and 10 = extreme) based on flooding risk to the building footprint. Flood risk is defined as a combination of cumulative risk over 30 years and flood depth. Flood depth is calculated at the lowest elevation of the building footprint (largest if more than 1 exists, or property centroid where footprint does not exist)
CS_depth_RP_YY	int	Climate Scenario (low, medium or high) by Flood depth (in cm) for the Return Period (2, 5, 20, 100 or 500) and Year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_depth_002_year00
CS_chance_flood_YY	float	Climate Scenario (low, medium or high) by Cumulative probability (percent) of at least one flooding event that exceeds the threshold at a threshold flooding depth in cm (0, 15, 30) for the year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_chance_00_year00
aal_YY_CS	int	The annualized economic damage estimate to the building structure from flooding by Year (today or 30 years in the future) by Climate Scenario (low, medium, high). Today as year00 and 30 years as year30. ex: aal_year00_low
hist1_id	int	A unique First Street identifier assigned to a historic storm event modeled by First Street
hist1_event	string	Short name of the modeled historic event
hist1_year	int	Year the modeled historic event occurred
hist1_depth	int	Depth (in cm) of flooding to the building from this historic event
hist2_id	int	A unique First Street identifier assigned to a historic storm event modeled by First Street
hist2_event	string	Short name of the modeled historic event
hist2_year	int	Year the modeled historic event occurred
hist2_depth	int	Depth (in cm) of flooding to the building from this historic event
adapt_id	int	A unique First Street identifier assigned to each adaptation project
adapt_name	string	Name of adaptation project
adapt_rp	int	Return period of flood event structure provides protection for when applicable
adapt_type	string	Specific flood adaptation structure type (can be one of many structures associated with a project)
fema_zone	string	Specific FEMA zone categorization of the property ex: A, AE, V. Zones beginning with "A" or "V" are inside the Special Flood Hazard Area which indicates high risk and flood insurance is required for structures with mortgages from federally regulated or insured lenders
footprint_flag	int	Statistics for the property are calculated at the centroid of the building footprint (1) or at the centroid of the parcel (0)

In 2023, New York had the highest number of public housing units among all the states in the United States. The number of housing units in New York was approximately *******, more than three times higher than the next two states in the ranking, Pennsylvania and Puerto Rico. In contrast, Vermont had the lowest number of public housing units, at just *** units. Additionally, Wyoming and Idaho were among the states with notably limited housing units available for occupancy, each reporting fewer than 1,000 units.

The 2020 Census Production Settings Demographic and Housing Characteristics (DHC) Approximate Monte Carlo (AMC) method seed Privacy Protected Microdata File (PPMF0) and PPMF replicates (PPMF1, PPMF2, ..., PPMF50) are a set of microdata files intended for use in estimating the magnitude of error(s) introduced by the 2020 Census Disclosure Avoidance System (DAS) into the 2020 Census Redistricting Data Summary File (P.L. 94-171), the Demographic and Housing Characteristics File, and the Demographic Profile.

The PPMF0 was the source of the publicly released, official 2020 Census data products referenced above, and was created by executing the 2020 DAS TopDown Algorithm (TDA) using the confidential 2020 Census Edited File (CEF) as the initial input; the official location for the PPMF0 is on the United States Census Bureau FTP server, but we also include a copy of it here for convenience. The replicates were then created by executing the 2020 DAS TDA repeatedly with the PPMF0 as its initial input.

Inspired by analogy to the use of bootstrap methods in non-private contexts, U.S. Census Bureau (USCB) researchers explored whether simple calculations based on comparing each PPMFi to the PPMF0 could be used to reliably estimate the scale of errors introduced by the 2020 DAS, and generally found this approach worked well.

The PPMF0 and PPMFi files contained here are provided so that external researchers can estimate properties of DAS-introduced error without privileged access to internal USCB-curated data sets; further information on the estimation methodology can be found in Ashmead et. al 2024.

The 2020 DHC AMC seed PPMF0 and PPMF replicates have been cleared for public dissemination by the USCB Disclosure Review Board (CBDRB-FY22-DSEP-004). The PPMF0 and PPMF replicates contain all Person and Units attributes necessary to produce the 2020 Census Redistricting Data Summary File (P.L. 94-171), the Demographic and Housing Characteristics File, and the Demographic Profile for both the United States and Puerto Rico, and include geographic detail down to the Census Block level. They do not include attributes specific to either the Detailed DHC-A or Detailed DHC-B products; in particular, data on Major Race (e.g., White Alone) is included, but data on Detailed Race (e.g., Cambodian) is not included in the PPMF0 and replicates.

See the average Airbnb revenue & other vacation rental data in San Juan in 2025 by property type & size, powered by Airbtics. Find top locations for investing.