Factors Affecting USA National Home Prices:

Overview:

This dataset contains a comprehensive collection of indicators which dictate the housing prices in the United States.

1. US Mortgage Rates:

• The average interest rates on mortgage loans in the United States.
• Used to track the cost of borrowing for housing and its impact on the real estate market.

2. Gross Domestic Product (GDP):

• The total monetary value of all goods and services produced within the United States during a specified period.
• A fundamental measure of economic performance, reflecting the overall economic health and growth trends of the country.

3. Unemployment Rates:

• The percentage of the labor force that is currently unemployed and actively seeking employment.
• A crucial indicator of labor market health and economic stability, influencing government policies and social welfare programs.

4. FED Funds Rate:

• The interest rate at which depository institutions lend reserve balances to other depository institutions overnight, as set by the Federal Reserve.
• This rate is a primary tool for monetary policy, influencing borrowing costs and, subsequently, overall economic activity.

5. Population Growth:

• The annual rate at which the U.S. population is changing, reflecting births, deaths, and migration.
• Offers insights into demographic trends, which have implications for labor force, consumer markets, and social services planning.

6. Consumer Price Index (CPI):

• A measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.
• A key indicator for assessing inflation or deflation, influencing consumer spending behavior and economic policy decisions.

S&P Case-Shiller Housing Price Index (USA):

• Measures changes in the prices of residential real estate properties over time, offering insight into the health and trends of the housing market in the United States.
• Crucial for assessing the state of the housing market, including property values, trends, and their impact on the broader economy.

30 Year Mortgage Rate in the United States decreased to 6.23 percent in November 26 from 6.26 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

Jumbo 30-Year Fixed Mortgage Rates

Zillow Home Value Forecast and Cash Buyer Data

By Zillow Data [source]

About this dataset

This dataset tracks the average jumbo mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate, jumbo mortgage in one-hour increments during business hours. It provides insight into changes in the housing market and helps consumers make wiser decisions with their investments. In addition to tracking monthly mortgage rates, our dataset also covers consumer's home types and housing stock, cash buyer data, Zillow Home Value Forecast (ZHVF), negative equity metrics, affordability forecasts for both mortgages and rents as well as historic data including historical ZHVI and household income. With this unique blend of financial and real estate information, users are empowered to make more informed decisions about their investments. The data is updated weekly with the most recent statistics available so that users always have access to up-to-date information

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How to use the dataset

How to Use This Dataset:

• To start exploring this dataset, identify what type of home you are interested in by selecting one of the four categories: “all homes” (Zillow defines all homes as single family, condominiums and coops with a county record); multifamily 5+; duplex/triplex; or condos/coops.
• Understand additional data products that are included such as Zillow Home Value Forecast (ZHVF), Cash Buyers % share, affordability metrics like mortgage affordability or rental affordability and historical ZHVI values along with its median value for particular households or geographies which needs deeper insights into other endogenous variables such detailed information like how many bedrooms a house has etc.

• Choose your geographic region on which you would want to collect more information– regions could include city breakdowns from nationwide level down till specific metropolitan etc . Also use special crosswalks available if needed between federally defined metrics for counties / metro areas combined with Zillow's own ones for greater accuracy when analysing external facors effect on data . To download all datasets at once - click here. .

• Gather more relevant external factors for analysis such as home values forecasts using our published methodology post given url , further to mention TransUnion credit bureau related debt amounts also consider median household incomes vis Bureaus of Labor Cost Indexes ; All these give us greater dimensional insights into market dynamics affecting any particular region finally culminating into deeper research findings when taken together . The reasons behind any fluctions observed can be properly derived as a result .

          Finally make sure that proper attribution is alwys done following mentioned Terms Of Use while downloading since 'All Data Accessed And Downloaded From This Page Is Free For Public Use By Consumers , Media
  

Research Ideas

• Using the Mortgage Rate Data to devise strategies to help persons purchasing jumbo mortgages determine the best time and rates to acquire a loan.
• Analyzing trends in the market by investigating changes in affordability over time by studying rent and mortgage affordability, price-to-income ratios, and historical ZHVIs with cash buyers.
• Comparing different areas of housing markets over diverse geographies using data on all homes, condos/co-ops, multifamily dwellings 5+ units, duplexes/triplexes across various counties or metro areas

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: MortgageRateJumboFixed.csv | Column name | Description | |:---------------------------|:---------------------------------------------------------------------------------------------------------------| | Date | The date of the mortgage rate. (Date) | | TimePeriod | The time period of the ...

Negative Equity in U.S. Housing Market

Measuring Home Values, Debt, and Credit Risk

By Zillow Data [source]

About this dataset

This dataset, Negative Equity in the US Housing Market, provides an in-depth look into the negative equity occurring across the United States during this single quarter. Included are metrics such as total amount of negative equity in millions of dollars, total number of homes in negative equity, percentage of homes with mortgages that are in negative equity and more. These data points provide helpful insights into both regional and national trends regarding the prevalence and rate of home mortgage delinquency stemming from a diminishment of value from peak levels.

Home types available for analysis include 'all homes', condos/co-ops, multifamily units containing five or more housing units as well as duplexes/triplexes. Additionally, Cash buyers rates for particular areas can also be determined by referencing this collection. Further metrics such as mortgage affordability rates and impacts on overall indebtedness are readily calculated using information related to Zillow's Home Value Index (ZHVI) forecast methodology and TransUnion data respectively.

Other variables featured within this dataset include characteristics like region type (i.e city, county ..etc), size rank based on population values , percentage change in ZHVI since peak levels as well as loan-to-value ratio greater than 200 across all regions constituted herein (NE). Moreover Zillow's own Secondary Mortgage Market Survey data is utilized to acquire average mortgage quote rates while correlative Census Bureau NCHS median household income figures represent typical assessable proportions between wages and debt obligations . So whether you're looking to assess effects along metro lines or detailed buffering through zip codes , this database should prove sufficient for insightful explorations! Nonetheless users must strictly adhere to all conditions encompassed within Terms Of Use commitments put forth by our lead provider before accessing any resources included herewith

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Research Ideas

• Analyzing regional and state trends in negative equity: Analyze geographic differences in the percentage of mortgages “underwater”, total amount of negative equity, number of homes at least 90 days late, and other key indicators to provide insight into the factors influencing negative equity across regions, states and cities.
• Tracking the recovery rate over time: Track short-term changes in numbers related to negative equity (e.g., region or area ZHVI Change from Peak) to monitor recovery rates over time as well as how different policy interventions are affecting homeownership levels in affected areas.
• Exploring best practices for promoting housing affordability: Compare affordability metrics (e.g., mortgage payments, price-to-income ratios) across different geographic locations over time to identify best practices for empowering homeowners and promoting stability within the housing market while reducing local inequality impacts related to availability of affordable housing options and access to credit markets like mortgages/loans etc

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: NESummary_2017Q1_Public.csv | Column name | Description | |:------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------| | RegionType | The type of region (e.g., city, county, metro etc.) (String) | | City | Name of the city (String) | | County | Name of the county (String) | | State | Name of the state (String) | | Metro ...

Existing Home Sales in the United States increased to 4100 Thousand in October from 4050 Thousand in September of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Research in modelling housing market dynamics using agent-based models (ABMs) has grown due to the rise of accessible individual-level data. This research involves forecasting house prices, analysing urban regeneration, and the impact of economic shocks. There is a trend towards using machine learning (ML) algorithms to enhance ABM decision-making frameworks. This study investigates exogenous shocks to the UK housing market and integrates reinforcement learning (RL) to adapt housing market dynamics in an ABM. Results show agents can learn real-time trends and make decisions to manage shocks, achieving goals like adjusting the median house price without pre-determined rules. This model is transferable to other housing markets with similar complexities. The RL agent adjusts mortgage interest rates based on market conditions. Importantly, our model shows how a central bank agent learned conservative behaviours in sensitive scenarios, aligning with a 2009 study, demonstrating emergent behavioural patterns.

The property listings dataset contains information about real estate properties available for sale or rent in Brazil. It includes details such as property type (apartment, house, commercial property), location (city, neighborhood), size (square footage, number of rooms), price, amenities, and contact information for the property owner or real estate agent. This dataset can be used for market analysis, property valuation, and identifying trends in the real estate market.

Sales and Rental Prices Dataset: The sales and rental prices dataset provides information about the prices of real estate properties in Brazil. It includes data on property transactions, including sale prices and rental prices per square meter or per month. This dataset can be used to analyze price trends, compare property prices across different regions, and identify areas with high or low real estate market demand.

Property Characteristics Dataset: The property characteristics dataset contains detailed information about the features and attributes of real estate properties. It includes data such as the number of bedrooms, bathrooms, parking spaces, floor plan, construction year, building amenities, and property condition. This dataset can be used for property classification, identifying popular property features, and evaluating property quality.

Geographical Data: Geographical data includes information about the location and spatial features of real estate properties in Brazil. It can include data such as latitude and longitude coordinates, zoning information, proximity to amenities (schools, hospitals, parks), and neighborhood demographics. This dataset can be used for spatial analysis, identifying hotspots or desirable locations, and understanding the neighborhood characteristics.

Property Market Trends Dataset: The property market trends dataset provides information about market conditions and trends in the real estate sector in Brazil. It includes data such as the number of property listings, average time on the market, price fluctuations, mortgage interest rates, and economic indicators that impact the real estate market. This dataset can be used for market forecasting, understanding market dynamics, and making informed investment decisions.

Real Estate Regulatory Data: Real estate regulatory data includes information about legal and regulatory aspects of the real estate sector in Brazil. It can include data on property ownership, property taxes, zoning regulations, building permits, and legal restrictions on property transactions. This dataset can be used for legal compliance, understanding property ownership rights, and assessing the legal framework for real estate transactions.

Historical Data: Historical real estate data includes past records and trends of property prices, market conditions, and sales volumes in Brazil. This dataset can span several years and can be used to analyze long-term market trends, compare current market conditions with historical data, and assess the performance of the real estate market over time.

Comprehensive dataset of Australian property market metrics including average home loan sizes, borrowing capacity calculations by income level, historical cash rate impact on borrowing capacity, state-level property prices, growth rates, and affordability ratios. Data sourced from Australian Bureau of Statistics (ABS), APRA, and RBA.

Negative Equity Trends in US Housing Markets

Time Series Data Across Regions and Housing Types

By Zillow Data [source]

About this dataset

• This unique dataset explores the trends in negative equity within US housing markets from 2011 to 2017, allowing users to uncover the various factors and determinants that affected the outcome in each market. With data provided on all home types such as single-family homes, condominiums, and co-ops, as well as special metrics such as cash buyers and affordability analyses, you will be able to gain a comprehensive understanding of how these forces have interacted over time. Using this data you can not only learn more about historical behavior but also make predictions for future trends in these impacts.

• In addition to data collected by Zillow through their own internal resources, they have also partnered with TransUnion and other affiliate sources to give an even more precise look into what has been driving these changing dynamics across US housing markets. Such information includes negative equity metrics which allow us to track actual outstanding home-related debt amounts over time - a valuable resource when evaluating potential investments or relocations!

• And of course with any dataset there are a few guiding principles that one should take note of before delving in – this is especially true when it comes down to copyright issues or prohibited uses; though all data can be freely obtained here for public use - clear attribution of such information is legally required at all times (as stated on Zillow’s very own Terms & Conditions page). Furthermore additional resources such as Mortgage Rate Series or Jumbo Mortgages are also available through Zillow; again making sure that appropriate disclaimers are read before utilizing them.

Regardless this little treasure trove of knowledge is waiting at your fingertips – whether you’re trying your luck investing wise or just looking for an area where renting rates are equitable compared real estate values; it provides everything you need understand regional housing market fluctuations over the last half decade!

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How to use the dataset

This dataset provides historical and current trends in negative equity (the amount a mortgage is underwater) across the United States. It contains negative equity data from Zillow, one of the leading real estate data providers. The dataset covers all housing types (including single family, condominiums and co-ops). Additionally, it includes cash buyers share, mortgage affordability index, rental affordability index and other relative measures of affordability for US metro areas. This guide will help you understand how to use this data set for your own analysis.

Overview of Covered Data:

The dataset contains time series data that shows your current trend in negative equity rate as well as some associated metrics across different scales such as region, county, city and MSA level. To access this information you will need to take following columns into consideration while using this data set:

• RegionName: Name of the region (e.g., city/county/MSA)
• SizeRank: Ranking of the region by size
• RegionType: Type of region (e.g., city/county/state)
• StateName: Name of the state
• MSA: Metropolitan Statistical Area FORMAT_4C A4 RINFOX_ RTI Information Exchange File Format [multi value 9] FORMAT_3E A3 FITS Flexible Image Transport System VERSION 4C 3E 1 Language Indicator 0 0 1 1 DONTCOPY 536880031 FILEEXTN 3 Stream Type buffer 'USTD' file version 2 HNEED 8 FILETYPE 'UDIO' creation date 05 FEB 1985 Source FMT0025 APPLICAT TRAINFORM File Organization Spooled Files DF140520 Header Block Length in Words 682 with Header Offset 636 / ULQUACK INTLCHAN * ETBFMT(V7R2),D*RECORD ACCOUNT CRFTIME FT240187 batch process status continuous Availability Continuous Version number V03C02 LOADAT AT04

Research Ideas

• Analyzing which markets have been disproportionately affected by the housing crisis and utilizing this information to inform investment strategies and...

Rental Affordability Analysis Based on Median Income

Trends in Tier-Based Affordability Across the U.S

By Zillow Data [source]

About this dataset

This dataset contains rental affordability data for different regions in the US, giving valuable insights into regional rental markets. Renters can use this information to identify where their budget will go the farthest. The cities are organized by rent tier in order to analyze affordability trends within and between different housing stock types. Within each region, the data includes median household income, Zillow Rent Index (ZRI), and percent of income spent on rent.

The Zillow Home Value Forecast (ZHVF) is used to calculate future combined mortgage pay/rent payments in each region using current median home prices, actual outstanding debt amounts and 30-year fixed mortgage interest rates reported through partnership with TransUnion credit bureau. Zillow also provides a breakdown of cash vs financing purchases for buyers looking for an investment or cash option solution.

This dataset provides an effective tool for consumers who want to better understand how their budget fits into diverse rental markets across the US; from condominiums and co-ops, multifamily residences with five or more units, duplexes and triplexes - every renter can determine how their housing budget should be adjusted as they consider multiple living possibilities throughout the country based on real-time price data!

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How to use the dataset

Introduction

Getting Started

• First, you'll need to download the TieredAffordability_Rental.csv dataset from this Kaggle page onto your computer or device.

• After downloading the data set onto your device, open it with any CSV viewing software of your choice (ex: Excel). It will include columns for RegionName**RegionName** , homes type/housing stock (All Homes or Condo/Co-op) SizeRank , Rent tier tier , Date date , median household income income , Zillow Rent Index zri and PercentIncomeSpentOnRent percentage (what portion of monthly median house-hold goes toward monthly mortgage payment) .

• To begin analyzing rental prices across different regions using this dataset, look first at column four: SizeRank; which ranks each region based on size - smallest regions listed first and largest at last - so that you can compare a similar range of Regions when looking at affordability by home sizes larger than one unit multiplex dwellings.*Duples/Triplex*. Once there is an understanding of how all homes compare overall now it is time to consider home types Multifamily 5+ units according to rent tiers tier .

• Next, choose one or more region(s) for comparison based on their rank in SizeRank column –so that all information gathered about them reflects what portionof households fall into certain categories ; eg; All Homes / Small Home /Large Home / MultiPlex Dwelling and what tier does each size rank falls into eg.: Affordable/Slightly Expensive/ Moderately Expensive etc.. This will enable further abstraction from other elements like date vs inflation rate per month or periodical intervals set herein by Rate segmentation i e dates givenin ‘Date’Columns – making the task easier and more direct while analyzing renatalAffordibility Analysis Based On Median Income zri 00 zwi & PCISOR 00 PCIRO

Research Ideas

• Use the PercentIncomeSpentOnRent column to compare rental affordability between regions within a particular tier and determine optimal rent tiers for relocating families.
• Analyze how market conditions are affecting rental affordability over time by using the income, zri, and PercentageIncomeSpentOnRent columns.
• Identify trends in housing prices for different tiers over the years by comparing SizeRank data with Zillow Home Value Forecast (ZHVF) numbers across different regions in order to identify locations that may be headed up or down in terms of home values (and therefore rent levels)

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: TieredAffordability_Rental.csv | Column name | Description | |:-----------------------------|:-------------------------------------------------------------| | RegionName | The name of the region. (String) ...

This comprehensive dataset encapsulates a wide array of information regarding home mortgage activities in Utah from 2018 to 2022. It includes detailed data points such as loan types, purposes, amounts, and applicant demographics. Key metrics like loan-to-value ratios, interest rates, and applicant credit scores offer deep insights into the housing loan market. Additionally, it covers varied loan characteristics, property values, and applicant details, reflecting the dynamics of Utah's mortgage landscape. This rich dataset is invaluable for analyzing trends, understanding market behaviors, and examining the impact of financial policies in Utah's real estate sector.

Context

The Federal Reserve sets interest rates to promote conditions that achieve the mandate set by the Congress — high employment, low and stable inflation, sustainable economic growth, and moderate long-term interest rates. Interest rates set by the Fed directly influence the cost of borrowing money. Lower interest rates encourage more people to obtain a mortgage for a new home or to borrow money for an automobile or for home improvement. Lower rates encourage businesses to borrow funds to invest in expansion such as purchasing new equipment, updating plants, or hiring more workers. Higher interest rates restrain such borrowing by consumers and businesses.

Content

This dataset includes data on the economic conditions in the United States on a monthly basis since 1954. The federal funds rate is the interest rate at which depository institutions trade federal funds (balances held at Federal Reserve Banks) with each other overnight. The rate that the borrowing institution pays to the lending institution is determined between the two banks; the weighted average rate for all of these types of negotiations is called the effective federal funds rate. The effective federal funds rate is determined by the market but is influenced by the Federal Reserve through open market operations to reach the federal funds rate target. The Federal Open Market Committee (FOMC) meets eight times a year to determine the federal funds target rate; the target rate transitioned to a target range with an upper and lower limit in December 2008. The real gross domestic product is calculated as the seasonally adjusted quarterly rate of change in the gross domestic product based on chained 2009 dollars. The unemployment rate represents the number of unemployed as a seasonally adjusted percentage of the labor force. The inflation rate reflects the monthly change in the Consumer Price Index of products excluding food and energy.

Acknowledgements

The interest rate data was published by the Federal Reserve Bank of St. Louis' economic data portal. The gross domestic product data was provided by the US Bureau of Economic Analysis; the unemployment and consumer price index data was provided by the US Bureau of Labor Statistics.

Inspiration

How does economic growth, unemployment, and inflation impact the Federal Reserve's interest rates decisions? How has the interest rate policy changed over time? Can you predict the Federal Reserve's next decision? Will the target range set in March 2017 be increased, decreased, or remain the same?

Fannie Mae and Freddie Mac Loan-Level Dataset

Borrower Demographics, Loan-to-Value Ratios, and Census Tract Location

By Natarajan Krishnaswami [source]

About this dataset

The FHFA Public Use Databases provide an unprecedented look into the flow of mortgage credit and capital in America's communities. With detailed information about the income, race, gender and census tract location of borrowers, this database can help lenders, planners, researchers and housing advocates better understand how mortgages are acquired by Fannie Mae and Freddie Mac.

This data set includes 2009-2016 single-family property loan information from the Enterprises in combination with corresponding census tract information from the 2010 decennial census. It allows for greater granularity in examining mortgage acquisition patterns within each MSA or county by combining borrower/property characteristics, such as borrower's race/ethnicity; co-borrower demographics; occupancy type; Federal guarantee program (conventional/other versus FHA-insured); age of borrowers; loan purpose (purchase, refinance or home improvement); lien status; rate spread between annual percentage rate (APR) and average prime offer rate (APOR); HOEPA status; area median family income and more.

In addition to demographic data on borrowers and properties, this dataset also provides insight into affordability metrics such as median family incomes at both the MSA/county level as well as functional owner occupied bankrupt tracts using 2010 Census based geography while taking into account American Community Survey estimates available at January 1st 2016. This allows us to calculate metrics that are important for assessing inequality such as tract income ratios which measure what portion of an area’s median family income is made up by a single borrows earnings or the ratio between borrows annual income compared to an area’s average median family iincome for those year’s reporting period. Finally each record contains Enterprise Flags associated with whether loans were purchased my Fannie Mae or Freddie Mac indicating further insights regarding who is financing policies affecting undocumented immigrant labor access as well affordable housing legislation targeted towards first time home buyers

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How to use the dataset

This guide will provide you with all the information needed to use the Fannie Mae and Freddie Mac Loan-Level Dataset for 2016. The dataset contains loan-level data for both Fannie Mae and Freddie Mac, including loans acquired in 2016. It includes details such as homeowner demographics, loan-to-value ratio, census tract location, and affordability of mortgage.

The first step to using this dataset is understanding how it is organized. There are 38 fields that make up the loan level data set, making it easy to understand what is being looked at. For each field there is a description of what the field represents and potential values it can take on (i.e., if it’s an integer or float). Having an understanding of the different fields will help when querying certain data points or comparing/contrasting.

Once you understand what type of information is available in this dataset you can start to create queries or visualizations that compare trends across Fannie Mae & Freddie Mac loans made in 2016. Depending on your interest areas such as homeownership rates or income disparities certain statistics may be pulled from the dataset such as borrower’s Annual Income Ratio per area median family income by state code or a comparison between Race & Ethnicity breakdown between borrowers and co-borrowers from various states respective MSAs, among other possibilities based on your inquiries . Visualizations should then be created so that clear comparisons and contrasts could be seen more easily by other users who may look into this same dataset for additional insights as well .

After creating queries/visualization , you can dive deeper into research about corresponding trends & any biases seen within these datasets related within particular racial groupings compared against US Postal & MSA codes used within the 2010 Census Tract locations throughout the US respectively by further utilizing publicly available research material that looks at these subjects with regards housing policies implemented through out years one could further draw conclusions depending on their current inquiries

Research Ideas

• Use the dataset to analyze borrowing patterns based on race, nationality and gender, to better understand the links between minority groups and access to credit...

Mortgage Application in the United States increased by 0.20 percent in the week ending November 21 of 2025 over the previous week. This dataset provides - United States MBA Mortgage Applications - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Mexico was last recorded at 7.25 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Mortgage Rate in Australia decreased to 5.51 percent in September from 5.52 percent in August of 2025. This dataset includes a chart with historical data for Australia Mortgage Rate.

This dataset provides values for INTEREST RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The benchmark interest rate in India was last recorded at 5.50 percent. This dataset provides - India Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Philippines was last recorded at 4.75 percent. This dataset provides the latest reported value for - Philippines Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.