Dataset Overview

This dataset provides historical housing price indices for the United States, covering a span of 20 years from January 2000 onwards. The data includes housing price trends at the national level, as well as for major metropolitan areas such as San Francisco, Los Angeles, New York, and more. It is ideal for understanding how housing prices have evolved over time and exploring regional differences in the housing market.

Why This Dataset?

The U.S. housing market has experienced significant shifts over the last two decades, influenced by economic booms, recessions, and post-pandemic recovery. This dataset allows data enthusiasts, economists, and real estate professionals to analyze long-term trends, make forecasts, and derive insights into regional housing markets.

What’s Included?

Time Period: January 2000 to the latest available data (specific end date depends on the dataset). Frequency: Monthly data. Regions Covered: 20+ U.S. cities, states, and aggregates.

Columns Description

Each column represents the housing price index for a specific region or aggregate, starting with a date column:

Date: Represents the date of the housing price index measurement, recorded with a monthly frequency. U.S. National: The national-level housing price index for the United States. 20-City Composite: The aggregate housing price index for the top 20 metropolitan areas in the U.S. CA-San Francisco: The housing price index for San Francisco, California. CA-Los Angeles: The housing price index for Los Angeles, California. WA-Seattle: The housing price index for Seattle, Washington. NY-New York: The housing price index for New York City, New York. Additional Columns: The dataset includes more columns with housing price indices for various U.S. cities, which can be viewed in the full dataset preview.

Potential Use Cases

Time-Series Analysis: Investigate long-term trends and patterns in housing prices. Forecasting: Build predictive models to forecast future housing prices using historical data. Regional Comparisons: Analyze how housing prices have grown in different cities over time. Economic Insights: Correlate housing prices with economic factors like interest rates, GDP, and inflation.

Who Can Use This Dataset?

This dataset is perfect for:

Data scientists and machine learning practitioners looking to build forecasting models. Economists and policymakers analyzing housing market dynamics. Real estate investors and analysts studying regional trends in housing prices.

Example Questions to Explore

Which cities have experienced the highest housing price growth over the last 20 years? How do housing price trends in coastal cities (e.g., Los Angeles, Miami) compare to midwestern cities (e.g., Chicago, Detroit)? Can we predict future housing prices using time-series models like ARIMA or Prophet?

House Price Index YoY in the United States decreased to 2.60 percent in June from 2.90 percent in May of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

Table B.3.1 presents quarterly mortgage rate data specific to the Irish market. These data include all euro and non-euro denominated mortgage lending in the Republic of Ireland only. New business refers to new mortgage lending drawdowns during the quarter, broken down by type of interest rate product (i.e. fixed, tracker and SVR). The data also provide further breakdown of mortgages for principal dwelling house (PDH) and buy-to-let (BTL) properties. Renegotiations of existing loans are not included.

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.

This dataset provides a comprehensive view of the Portuguese housing market, integrating both listing and official transaction data. Initially compiled from historical reports by Idealista, it includes €/m² prices for sales and rentals across various Portuguese regions.

Now, this dataset has been significantly enhanced with official transaction data from the Instituto Nacional de Estatística (INE) of Portugal. This addition includes quarterly values and counts of housing transactions at a national level, providing a crucial perspective on actual market activity beyond listing prices.

This consolidated dataset is a core component of a broader case study exploring housing affordability, investment potential, and regional development across Portugal. It enables a more robust analysis by allowing comparison between asking prices and actual transaction values, as well as insights into market volume.

Additional socioeconomic data will be gradually integrated to further enrich the analysis, such as:

• Minimum and average wages (INE)
• New construction volume (INE)
• Migration and population trends
• Interest rates (Banco de Portugal)

🔗 Full pipeline and source files, including data cleaning scripts and analysis notebooks, are available on GitHub: https://github.com/igor-marques/portugal-housing-market-capstone

Data Sources Included: * Idealista: Historical listing prices (€/m²) for sales and rentals across Portuguese regions. * Instituto Nacional de Estatística (INE): Official quarterly data on housing transaction values and counts for Portugal (from Q1 2009 to Q1 2025).

30 Year Mortgage Rate in the United States decreased to 6.50 percent in September 4 from 6.56 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Interest Rates, High Prices, and Inventory Shortage to Slow Down Housing Market

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Graph and download economic data for All-Transactions House Price Index for Colorado (COSTHPI) from Q1 1975 to Q2 2025 about CO, appraisers, HPI, housing, price index, indexes, price, and USA.

The average resale house price in Canada was forecast to reach nearly ******* Canadian dollars in 2026, according to a January forecast. In 2024, house prices increased after falling for the first time since 2019. One of the reasons for the price correction was the notable drop in transaction activity. Housing transactions picked up in 2024 and are expected to continue to grow until 2026. British Columbia, which is the most expensive province for housing, is projected to see the average house price reach *** million Canadian dollars in 2026. Affordability in Vancouver Vancouver is the most populous city in British Columbia and is also infamously expensive for housing. In 2023, the city topped the ranking for least affordable housing market in Canada, with the average homeownership cost outweighing the average household income. There are a multitude of reasons for this, but most residents believe that foreigners investing in the market cause the high housing prices. Victoria housing market The capital of British Columbia is Victoria, where housing prices are also very high. The price of a single family home in Victoria's most expensive suburb, Oak Bay was *** million Canadian dollars in 2024.

Danish residential house prices (1992-2024)

About the dataset (cleaned data)

The dataset (parquet file) contains approximately 1,5 million residential household sales from Denmark during the periode from 1992 to 2024. All cleaned data is merged into one parquet file here on Kaggle. Note some cleaning might still be nessesary, see notebook under code.

Also, added a random sample (100k) of the dataset as a csv file.

Done in Python version: 2.6.3.

Raw data

Raw data and more info is avaible on Github repositary: https://github.com/MartinSamFred/Danish-residential-housingPrices-1992-2024.git

The dataset has been scraped and cleaned (to some extent). Cleaned files are located in: \Housing_data_cleaned \ named DKHousingprices_1 and 2. Saved in parquet format (and saved as two files due to size).

Cleaning from raw files to above cleaned files is outlined in BoligsalgConcatCleanigGit.ipynb. (done in Python version: 2.6.3)

Webscraping script: Webscrape_script.ipynb (done in Python version: 2.6.3)

Provided you want to clean raw files from scratch yourself:

Uncleaned scraped files (81 in total) are located in \Housing_data_raw \ Housing_data_batch1 and 2. Saved in .csv format and compressed as 7-zip files.

Additional files added/appended to the Cleaned files are located in \Addtional_data and named DK_inflation_rates, DK_interest_rates, DK_morgage_rates and DK_regions_zip_codes. Saved in .xlsx format.

Content

Each row in the dataset contains a residential household sale during the period 1992 - 2024.

“Cleaned files” columns:

0 'date': is the transaction date

1 'quarter': is the quarter based on a standard calendar year

2 'house_id': unique house id (could be dropped)

3 'house_type': can be 'Villa', 'Farm', 'Summerhouse', 'Apartment', 'Townhouse'

4 'sales_type': can be 'regular_sale', 'family_sale', 'other_sale', 'auction', '-' (“-“ could be dropped)

5 'year_build': range 1000 to 2024 (could be narrowed more)

6 'purchase_price': is purchase price in DKK

7 '%_change_between_offer_and_purchase': could differ negatively, be zero or positive

8 'no_rooms': number of rooms

9 'sqm': number of square meters

10 'sqm_price': 'purchase_price' divided by 'sqm_price'

11 'address': is the address

12 'zip_code': is the zip code

13 'city': is the city

14 'area': 'East & mid jutland', 'North jutland', 'Other islands', 'Capital, Copenhagen', 'South jutland', 'North Zealand', 'Fyn & islands', 'Bornholm'

15 'region': 'Jutland', 'Zealand', 'Fyn & islands', 'Bornholm'

16 'nom_interest_rate%': Danish nominal interest rate show pr. quarter however actual rate is not converted from annualized to quarterly

17 'dk_ann_infl_rate%': Danish annual inflation rate show pr. quarter however actual rate is not converted from annualized to quarterly

18 'yield_on_mortgage_credit_bonds%': 30 year mortgage bond rate (without spread)

Uses

Various (statistical) analysis, visualisation and I assume machine learning as well.

Practice exercises etc.

Uncleaned scraped files are great to practice cleaning, especially string cleaning. I’m not an expect as seen in the coding ;-).

Disclaimer

The data and information in the data set provided here are intended to be used primarily for educational purposes only. I do not own any data, and all rights are reserved to the respective owners as outlined in “Acknowledgements/sources”. The accuracy of the dataset is not guaranteed accordingly any analysis and/or conclusions is solely at the user's own responsibly and accountability.

Acknowledgements/sources

All data is publicly available on:

Boliga: https://www.boliga.dk/

Finans Danmark: https://finansdanmark.dk/

Danmarks Statistik: https://www.dst.dk/da

Statistikbanken: https://statistikbanken.dk/statbank5a/default.asp?w=2560

Macrotrends: https://www.macrotrends.net/

PostNord: https://www.postnord.dk/

World Data: https://www.worlddata.info/

Dataset picture / cover photo: Nick Karvounis (https://unsplash.com/)

Have fun… :-)

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Understanding the Dynamics and Implications of a Housing Market Recession

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Average House Prices in Canada increased to 688700 CAD in July from 688500 CAD in June of 2025. This dataset includes a chart with historical data for Canada Average House Prices.

Extract detailed property data points — address, URL, prices, floor space, overview, parking, agents, and more — from any real estate listings. The Rankings data contains the ranking of properties as they come in the SERPs of different property listing sites. Furthermore, with our real estate agents' data, you can directly get in touch with the real estate agents/brokers via email or phone numbers.

A. Usecase/Applications possible with the data:

1. Property pricing - accurate property data for real estate valuation. Gather information about properties and their valuations from Federal, State, or County level websites. Monitor the real estate market across the country and decide the best time to buy or sell based on data

2. Secure your real estate investment - Monitor foreclosures and auctions to identify investment opportunities. Identify areas within special economic and opportunity zones such as QOZs - cross-map that with commercial or residential listings to identify leads. Ensure the safety of your investments, property, and personnel by analyzing crime data prior to investing.

3. Identify hot, emerging markets - Gather data about rent, demographic, and population data to expand retail and e-commerce businesses. Helps you drive better investment decisions.

4. Profile a building’s retrofit history - a building permit is required before the start of any construction activity of a building, such as changing the building structure, remodeling, or installing new equipment. Moreover, many large cities provide public datasets of building permits in history. Use building permits to profile a city’s building retrofit history.

5. Study market changes - New construction data helps measure and evaluate the size, composition, and changes occurring within the housing and construction sectors.

6. Finding leads - Property records can reveal a wealth of information, such as how long an owner has currently lived in a home. US Census Bureau data and City-Data.com provide profiles of towns and city neighborhoods as well as demographic statistics. This data is available for free and can help agents increase their expertise in their communities and get a feel for the local market.

7. Searching for Targeted Leads - Focusing on small, niche areas of the real estate market can sometimes be the most efficient method of finding leads. For example, targeting high-end home sellers may take longer to develop a lead, but the payoff could be greater. Or, you may have a special interest or background in a certain type of home that would improve your chances of connecting with potential sellers. In these cases, focused data searches may help you find the best leads and develop relationships with future sellers.

How does it work?

• Analyze sample data
• Customize parameters to suit your needs
• Add to your projects
• Contact support for further customization

Dividend-Per-Share Time Series for Japan Real Estate Investment Corp. Japan Real Estate Investment Corporation (the "Company") was established on May 11, 2001 pursuant to Japan's Act on Investment Trusts and Investment Corporations ("ITA"). The Company was listed on the real estate investment trust market of the Tokyo Stock Exchange ("TSE") on September 10, 2001 (Securities Code: 8952). Since its IPO, the size of the Company's assets (total acquisition price) has grown steadily, expanding from 92.8 billion yen to 1,167.7 billion yen as of March 31, 2025. Over the same period, the Company's portfolio has also increased from 20 properties to 77 properties. During the March 2025 period (October 1, 2024 to March 31, 2025), the Japanese economy continued to demonstrate a gradual recovery, despite some lingering stagnation in capital investment and personal consumption due to inflation and other factors. On the other hand, given the policy rate hikes by the Bank of Japan, the shift in global interest rates to a lowering phase, the impact of U.S. policy trends, such as trade policy and other factors, interest rate trends, overseas political and economic developments, and price trends, including resource prices, will continue to bear watching. In the office leasing market, demand continues to grow for leases driven by business expansion and relocations aimed at improving location. As a result, the vacancy rate in central Tokyo continues to decline gradually. In addition, rent levels are rising at an accelerating rate. In light of the prevailing conditions in the leasing market, the Company is striving to attract new tenants through strategic leasing activities and to further enhance the satisfaction level of existing tenants by adding value to its portfolio properties with the aim of maintaining and improving the occupancy rate and realizing sustainable income growth across the entire portfolio. In the real estate trading market, despite the Bank of Japan normalizing its monetary policy, the appetite for property acquisition among both domestic and foreign investors remains firm, backed ma

This dataset contains an history of nearly all of the real estate transactions concerning a single house/appartment in France from 2014 to today. Some variables likely to have an impact on the price of real estate are also provided as time series: the households income levels per city, the average debt level of french peoples, the average amount of savings of french people, the interest rates of loans, the price of the rent per city, the number of houses and number of vacant houses per city.

This dataset is provided under a permissive licence, and is free to use for commercial uses. It has a vocation of helping research concerning the dynamics of real estate prices.

The dataset consist in extraction from several openly available datasets put together in a practical format: The DVF+ database of real estate transactions, the IRCOM dataset of household incomes and income taxes, average interest rates of real estate loans from the banque de france website, the LOVAC dataset of number of vacant and occupied housings per city, the OECD dataset of financial assets per capita, the "carte des loyers" dataset of 2018 and 2022 which list the average price of the rent per square meter, the Indice de Référence des Loyers (IRL) time series which is an index defining the maximum rent increase that can be applied to an already rented housing and is calculated every 3 months as the inflation adjusted buying power of 100€ in 1998, the TEC00104 eurostat dataset of debt levels.

Net-Interest-Income Time Series for Provident Financial Services Inc. Provident Financial Services, Inc. operates as the bank holding company for Provident Bank that provides various banking products and services to individuals, families, and businesses in the United States. Its deposit products include savings, checking, interest-bearing checking, money market deposit, and certificate of deposit accounts, as well as IRA products. The company's loan portfolio comprises commercial real estate loans that are secured by properties, such as multi-family apartment buildings, retail and industrial properties, and office buildings; commercial business loans; fixed-rate and adjustable-rate mortgage loans collateralized by one- to four-family residential real estate properties; commercial construction loans; and consumer loans consisting of home equity loans, home equity lines of credit, personal loans and unsecured lines of credit, and auto and recreational vehicle loans. It also offers cash management, remote deposit capture, payroll origination, escrow account management, and online and mobile banking services; and business credit cards. In addition, the company provides wealth management services comprising investment management, trust and estate administration, financial planning, and tax compliance and planning. Further, it sells insurance and investment products, including annuities; and manages and sells real estate properties acquired through foreclosure. The company was founded in 1839 and is headquartered in Jersey City, New Jersey.

Minority-Interest-Expense Time Series for Provident Financial Services Inc. Provident Financial Services, Inc. operates as the bank holding company for Provident Bank that provides various banking products and services to individuals, families, and businesses in the United States. Its deposit products include savings, checking, interest-bearing checking, money market deposit, and certificate of deposit accounts, as well as IRA products. The company's loan portfolio comprises commercial real estate loans that are secured by properties, such as multi-family apartment buildings, retail and industrial properties, and office buildings; commercial business loans; fixed-rate and adjustable-rate mortgage loans collateralized by one- to four-family residential real estate properties; commercial construction loans; and consumer loans consisting of home equity loans, home equity lines of credit, personal loans and unsecured lines of credit, and auto and recreational vehicle loans. It also offers cash management, remote deposit capture, payroll origination, escrow account management, and online and mobile banking services; and business credit cards. In addition, the company provides wealth management services comprising investment management, trust and estate administration, financial planning, and tax compliance and planning. Further, it sells insurance and investment products, including annuities; and manages and sells real estate properties acquired through foreclosure. The company was founded in 1839 and is headquartered in Jersey City, New Jersey.

The Residential Property Price Index in Australia rose by 4.7 percent qoq in Q4 2021, above market consensus of 3.9 percent and after a 5.0 percent growth in Q3. This was the sixth straight quarter of growth in property prices, supported by record-low interest rates and strong demand. The strongest quarterly price increases were recorded in Brisbane (9.6 percent), followed by Adelaide (6.8 percent), Hobart (6.5 percent), and Canberra (6.4 percent). Through the year to Q4, the index jumped to a record high of 23.7 percent, with Hobart, Canberra, Brisbane, Sydney, and Adelaide having the largest annual rise since the commencement of the series; while Melbourne had the largest annual rise since Q2 2010. This dataset includes a chart with historical data for Australia House Price Index QoQ.

Abstract copyright UK Data Service and data collection copyright owner.The English Housing Survey (EHS) is a continuous national survey commissioned by the Ministry of Housing, Community and Local Government (MHCLG) that collects information about people's housing circumstances and the condition and energy efficiency of housing in England. The EHS brings together two previous survey series into a single fieldwork operation: the English House Condition Survey (EHCS) (available from the UK Data Archive under GN 33158) and the Survey of English Housing (SEH) (available under GN 33277). The EHS covers all housing tenures. The information obtained through the survey provides an accurate picture of people living in the dwelling, and their views on housing and their neighbourhoods. The survey is also used to inform the development and monitoring of the Ministry's housing policies. Results from the survey are also used by a wide range of other users including other government departments, local authorities, housing associations, landlords, academics, construction industry professionals, consultants, and the general public. The EHS has a complex multi-stage methodology consisting of two main elements; an initial interview survey of around 12,000 households and a follow-up physical inspection. Some further elements are also periodically included in or derived from the EHS: for 2008 and 2009, a desk-based market valuation was conducted of a sub-sample of 8,000 dwellings (including vacant ones), but this was not carried out from 2010 onwards. A periodic follow-up survey of private landlords and agents (the Private Landlords Survey (PLS)) is conducted using information from the EHS interview survey. Fuel Poverty datasets are also available from 2003, created by the Department for Energy and Climate Change (DECC). The EHS interview survey sample formed part of the Integrated Household Survey (IHS) (available from the Archive under GN 33420) from April 2008 to April 2011. During this period the core questions from the IHS formed part of the EHS questionnaire. End User Licence and Special Licence Versions: From 2014 data onwards, the End User Licence (EUL) versions of the EHS will only include derived variables. In addition the number of variables on the new EUL datasets has been reduced and disclosure control increased on certain remaining variables. New Special Licence versions of the EHS will be deposited later in the year, which will be of a similar nature to previous EHS EUL datasets and will include derived and raw datasets. Further information about the EHS and the latest news, reports and tables can be found on the GOV.UK English Housing Survey web pages. The English Housing Survey, 2013: Housing Stock Data is available for all cases where a physical survey has been completed. For occupied cases the data comprises information from the household interview and from the physical survey. For vacant properties only, data from the physical survey are provided. The data are made available for a two-year rolling sample i.e. approximately 12,000 cases together with the appropriate two-year weights. For example, the EHS Housing Stock results presented here are for 2013, but cover the period April 2012 to March 2014. The Housing Stock dataset should be used for any analysis requiring information relating to the physical characteristics and energy efficiency of the housing stock. Derived datasets provide key analytical variables compiled post-fieldwork including energy efficiency ratings, decent home indicators and equivalised income. Latest edition information For the second edition (March 2017), a new cavity wall insulation variable wins95x was added to the physical file. This variable was introduced for the latest EHS Headline Report. From the submission of the 2015 EHS, wins95x will replace wins90x; it has been added to EHS physical files from 2007/8 onwards. Main Topics: The EHS Housing Stock survey consists of two components. Interview Survey An interview is first conducted with the householder. The interview topics include: general tenure and demographics; household income and housing costs; housing needs; housing aspirations and satisfaction; housing moves; and vulnerable and disadvantaged households. Physical Survey Where interviews are achieved (the 'full household sample'), each year all rented properties and a sub-sample of owner occupied properties are regarded as eligible for the physical survey and the respondent's consent is sought. A proportion of vacant properties are also sub-sampled. For these cases a visual inspection of the property, both internal and external is carried out by a qualified surveyor. Data collected cover: stock profile; amenities; services and the local environment; dwelling condition and safety; energy performance; and energy-inefficient dwellings. Multi-stage stratified random sample Face-to-face interview Physical measurements House inspection; Surveyor property inspection. 2013 2014 AGE AIDS FOR THE DISABLED ANXIETY APARTMENTS ATTITUDES BATHROOMS BEDROOMS BIOFUELS BOILERS BUILDING MAINTENANCE CAR PARKING AREAS CARS CEILINGS CENTRAL HEATING CHIMNEYS COHABITATION COMMUNAL ESTABLISHM... COOKING FACILITIES COSTS COUNCIL TAX DISABILITIES DISABLED ACCESSIBILITY DISABLED FACILITIES DISABLED PERSONS DOMESTIC SAFETY DOORS ECONOMIC ACTIVITY ECONOMIC VALUE EDUCATIONAL BACKGROUND ELDERLY ELECTRIC POWER SUPPLY EMPLOYEES EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ENERGY CONSUMPTION ENERGY EFFICIENCY ENVIRONMENT ETHNIC GROUPS England FAMILIES FINANCIAL RESOURCES FIRE FLOORS FOSSIL FUELS FREEHOLD FURNISHED ACCOMMODA... GARAGES GAS SUPPLY GENDER HAPPINESS HEADS OF HOUSEHOLD HEATING SYSTEMS HIGH RISE FLATS HOME BUILDINGS INSU... HOME BUYING HOME CONTENTS INSUR... HOME OWNERSHIP HOME SHARING HOMELESSNESS HOURS OF WORK HOUSE PRICES HOUSEHOLD INCOME HOUSEHOLDERS HOUSEHOLDS HOUSES HOUSING HOUSING AGE HOUSING BENEFITS HOUSING CONDITIONS HOUSING FACILITIES HOUSING IMPROVEMENT HOUSING SHORTAGES HOUSING TENURE HUMAN SETTLEMENT Housing ILL HEALTH INCOME INTEREST RATES KITCHENS LANDLORDS LAVATORIES LEASEHOLD LIFE SATISFACTION LOANS LOCAL TAX BENEFITS LODGERS MARITAL STATUS METHODS OF PAYMENT MORTGAGE ARREARS MORTGAGE PROTECTION... MORTGAGES OWNERSHIP AND TENURE PHYSICAL MOBILITY PLACE OF BIRTH POVERTY PRIVATE GARDENS PROPERTY RADIATORS RATES RENTED ACCOMMODATION RENTS RESIDENTIAL BUILDINGS RESIDENTIAL MOBILITY RESPONSIBILITY ROOFS ROOMS RURAL AREAS SATISFACTION SAVINGS SECOND HOMES SELF EMPLOYED SEWAGE DISPOSAL AND... SHELTERED HOUSING SINGLE OCCUPANCY HO... SOCIAL HOUSING SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS SOLAR ENERGY SPOUSES STANDARD OF LIVING STATUS IN EMPLOYMENT STRUCTURAL ELEMENTS... STUDENT HOUSING SUPERVISORY STATUS TEMPORARY EMPLOYMENT TENANCY AGREEMENTS THERMAL INSULATION TIED HOUSING TRAFFIC NOISE UNEMPLOYED UNFURNISHED ACCOMMO... UNWAGED WORKERS URBAN AREAS VACANT HOUSING WALLS WASHING FACILITIES WHEELCHAIRS WINDOWS

Change-In-Other-Working-Capital Time Series for Provident Financial Services Inc. Provident Financial Services, Inc. operates as the bank holding company for Provident Bank that provides various banking products and services to individuals, families, and businesses in the United States. Its deposit products include savings, checking, interest-bearing checking, money market deposit, and certificate of deposit accounts, as well as IRA products. The company's loan portfolio comprises commercial real estate loans that are secured by properties, such as multi-family apartment buildings, retail and industrial properties, and office buildings; commercial business loans; fixed-rate and adjustable-rate mortgage loans collateralized by one- to four-family residential real estate properties; commercial construction loans; and consumer loans consisting of home equity loans, home equity lines of credit, personal loans and unsecured lines of credit, and auto and recreational vehicle loans. It also offers cash management, remote deposit capture, payroll origination, escrow account management, and online and mobile banking services; and business credit cards. In addition, the company provides wealth management services comprising investment management, trust and estate administration, financial planning, and tax compliance and planning. Further, it sells insurance and investment products, including annuities; and manages and sells real estate properties acquired through foreclosure. The company was founded in 1839 and is headquartered in Jersey City, New Jersey.