House Price Index YoY in the United States decreased to 2.30 percent in July from 2.70 percent in June of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

Key information about House Prices Growth

• US house prices grew 5.2% YoY in Dec 2024, following an increase of 5.4% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Dec 2024, with an average growth rate of 5.4%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

Housing Index in Hong Kong decreased to 141.09 points in October 19 from 141.92 points in the previous week. This dataset provides - Hong Kong House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This study analyses the effects of oil price and macroeconomic shocks on the Malaysian housing market using a SVAR framework. The specification of the baseline model is based on standard economic theory. The Gregory-Hansen (GH) cointegration tests reveal that there is no cointegration among the variables of interest. Results from performing Toda-Yamamoto (TY) non-Granger causality tests show that oil price, labor force and inflation are the leading factors causing movements in the Malaysian housing prices in the long run. The findings from estimating generalized impulse response functions (IRFs) and variance decompositions (VDCs) indicate that oil price and labor force shocks explain a substantial portion of housing market price fluctuations in Malaysia.

Housing Index in Canada decreased to 122.70 points in September from 122.90 points in August of 2025. This dataset provides - Canada New Housing Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Housing Index in the United Kingdom decreased to 514.20 points in September from 515.60 points in August of 2025. This dataset provides - United Kingdom House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q2 2025 about sales, housing, and USA.

This table shows inflation, derived inflation and underlying inflation rates. Underlying inflation equals the inflation or derived inflation, excluding certain volatile items or series that are affected by factors other than general economic conditions, for example prices of fuel, vegetables, fruit and government taxes. Data available from: January 2006 till December 2015 Status of the figures: The figures in this table are final. Changes as of 16 June 2016: None, this table is stopped. Changes as of 10 December 2015: On 1 October 2015, the points system for the pricing of rental homes was adjusted by the Dutch national government. As a direct consequence, rental prices of a limited number of dwellings were reduced, which had a downward effect on the average rental price. The effect of this decrease on the rental price indices and imputed rent value could not be determined in time because housing associations announced the impact of rent adjustments only in November. For this reason, the figures of the groups 04100 ‘Actual rentals for housing’ and 04200 ‘Imputed rent value’ over October 2015 have now been adjusted. The figures of the groups 061100 ‘Pharmaceutical products’, 061200 ‘Other medical products, equipment’, 072200 ‘Fuels and lubricants’ and 083000 ‘Telephone and internet services’ over the months June through September 2015 have been corrected. This has no impact on the headline indices. The derived CPI decreased by 0.01 index point over August 2015.

Graph and download economic data for Real Residential Property Prices for Australia (QAUR628BIS) from Q1 1970 to Q2 2025 about Australia, residential, HPI, housing, real, price index, indexes, and price.

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.

How does CPI affect housing?

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

Housing Index in Romania increased to 162.38 points in the second quarter of 2025 from 161.75 points in the first quarter of 2025. This dataset provides - Romania House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

House Price Index YoY in the United Kingdom decreased to 1.30 percent in September from 2 percent in August of 2025. This dataset includes a chart with historical data for the United Kingdom House Price Index YoY.

Rent Inflation in the United States remained unchanged at 3.60 percent in September. This dataset includes a chart with historical data for the United States Rent Inflation.

Graph and download economic data for Commercial Real Estate Prices for United States (COMREPUSQ159N) from Q1 2005 to Q1 2025 about real estate, commercial, rate, and USA.

The Chinese economy has undergone a long-term transition reform, but there is still a planned economy characteristic in the financial sector, which is financial repression. Due to the existence of financial repression, China’s actual interest rate level should be lower than the Consumer Price Index (CPI). However, based on official China’s interest rates and CPI, over half of the years China’s actual interest rate remained higher than CPI by our calculation from 1999 to 2022. This is inconsistent with the financial repression that exists in China, and the main reason is the calculation methods of China’s CPI. China’s CPI measurement system originated from the planned economy era, which did not fully consider the rise in housing purchase prices, so the current CPI measurement system can be more realistically presented by taking the rise in housing prices into consider. The core idea of this study is to mining relevant official statistical data and calculate the proportion of Chinese residents’ expenditure on purchasing houses to their total expenditure. By taking the proportion of house purchases as the weight of house price factor, and taking the proportion of other consumption as the weight of official CPI, the Generalized CPI (GCPI) is formulated. The GCPI is then compared with market interest rates to determine the actual interest rate situation in China over the past 20 years. This study has found that if GCPI is used as a measure, China’s real interest rates have been negative for most years since 1999. Chinese residents have suffered the negative effects of financial repression over the past 20 years, and their property income cannot keep up with the actual losses caused by inflation. Therefore, it is believed that China’s CPI calculation method should be adjusted to take into account the rise in housing prices, so China’s actual inflation level could be more accurately reflected. In view of the above, deepening interest rate marketization reform and expand channels for financial investment are the future development goals of China’s financial system.

This table shows inflation, derived inflation and underlying inflation rates. Underlying inflation equals the inflation or derived inflation, excluding certain volatile items or series that are affected by factors other than general economic conditions, for example prices of fuel, vegetables, fruit and government taxes.

Data available from: January 2006 till December 2015

Status of the figures: The figures in this table are final.

Changes as of 16 June 2016: None, this table is stopped.

Changes as of 10 December 2015: On 1 October 2015, the points system for the pricing of rental homes was adjusted by the Dutch national government. As a direct consequence, rental prices of a limited number of dwellings were reduced, which had a downward effect on the average rental price. The effect of this decrease on the rental price indices and imputed rent value could not be determined in time because housing associations announced the impact of rent adjustments only in November. For this reason, the figures of the groups 04100 ‘Actual rentals for housing’ and 04200 ‘Imputed rent value’ over October 2015 have now been adjusted.

The figures of the groups 061100 ‘Pharmaceutical products’, 061200 ‘Other medical products, equipment’, 072200 ‘Fuels and lubricants’ and 083000 ‘Telephone and internet services’ over the months June through September 2015 have been corrected. This has no impact on the headline indices.

The derived CPI decreased by 0.01 index point over August 2015.

Background

I've been sitting on this for quite a while and it's a project that I'm glad that I attempted because it forced me to learn new things through trial and error in order to enrich the dataset. Like mapping variables based on set/dict of values for the CPI, lease and GNI adjustment columns. Like trying to scrape Google Maps source code and getting blocked from their maps for a while before finding out how to use other geocoders responsibly. Like running multiple simulations of different linreg algorithms and analysing their variance contributions.

Anyway, this dataset can be used to create rich data visualisations or you can try using it for machine learning because of the large sample size in a small geographic area.

Dataset contents

ALL Prices 1990-2021 Mar.csv

The largest file by far, ALL Prices 1990-2021 Mar.csv, contains over 800k rows of transactions of Singapore Housing Development Board (HDB) resale flats. As the name implies, BTO, SERS, HUDC and private housing are not included, though resale DBSS flat transactions are treated as ordinary HDB flat transactions. Many of the columns in the file are calculated columns or mapped columns (based on supplementary information) like the CPI index and lease percentage columns. For the full metadata/glossary of how I derived these terms, see the bottom of this description and/or the file and column descriptions.

Balas Table.csv

Balas Table.csv contains the ratios of leasehold land value to freehold land value for each year of remaining lease, from 1 to 99. This table is used by Singapore Land Authority (SLA) in determining land valuations which affect property value since most land in Singapore is leasehold. As there are some mistakes/anomalies in the dataset with 100 and 101 years, I used the maximum values of 96% ratio when mapping in the 2nd version so please don't use the old version.

MAS Core Inflation.csv

This file contains SIngapore's core CPI index value for each month from January 1990 to February 2021 as compiled by MAS. For March 2021 and subsequent future transactions, you have to make estimates and also update this table based on new releases by MAS. For this dataset, I used March 2021=100.4.

complete.csv

Contains all UNIQUE BLOCK addresses, along with their geocoder-supplied full address (inconsistent, many missing) and more importantly, their latitude and longitude coordinates. As there are 9000+ addresses, they were first geocoded using a mix of Photon and Google Maps source code scraping (more accurate but doesn't give full address for quick checking). Then, I manually looked through the addresses and coordinates to find and update all blatantly wrong (outside Singapore or wrong neighbourhood) and most slightly inaccurate (correct neighbourhood and/or street but tagged to wrong block number) for a total of around 1600 addresses, many in Whampoa/Boon Keng, Sengkang, Yishun and Woodlands.

gni per capita.csv

As the name implies, this file contains Singapore's GNI per capita in nominal S$ for the years 1990-2020. For 2021, you have to make an estimate based on the projected economic recovery from COVID-19 until the actual value is released. For this dataset, I used 2021=75000.

HDB machine learning.xlsx

This file is my own basic analysis of (numerical) variables that potentially help to determine the final resale price (measured by inflation-adjusted price per square metre). I used 5 linear regression algorithms and tested each variable individually as well as tried to maximise the R^2 using multiple linear regression with as many relevant variables as possible. I also included the correlation matrix between all the variables and that for relevant variables which helps in calculating the incremental contribution to variance for each variable.

Usefulness in training models?

As i've shown in the "HDB machine learning.xlsx" workbook, some variables are more influential than others but even the amount of variance contributed changes depending on the conditions applied. Various multiple linear regression models i've tried can only post up to 0.60+ in combined R^2, which means that up to 40% of variance in the inflation-adjusted price per square metre/foot flat prices is essentially just random noise or could have another hidden variable! Perhaps you can try to find another strongly related variable? Some ideas are proximity to MRTs/bus stops, ratio of HDB to private housing, average household size, other housing to population ratio indicators? It's important to consider whether correlated factors are causes or effects as well.

More importantly, do you think you can train a model to post much better numbers than multiple linear regression?

Metadata for ALL Prices file

Original columns: `month, town, flat_type, block, street_name, storey_range, area_sqm, flat_model...

Construction Output Price Indices (OPIs) from January 2014 to June 2025, UK. Summary

Rent Inflation in Canada increased to 4.80 percent in September from 4.50 percent in August of 2025. This dataset includes a chart with historical data for Canada Rent Inflation.

Inflation Rate in Singapore increased to 0.70 percent in September from 0.50 percent in August of 2025. This dataset provides - Singapore Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.