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

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.

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.

House Price Index YoY in the United States decreased to 1.70 percent in September from 2.40 percent in August of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

The benchmark interest rate in Sweden was last recorded at 1.75 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The real estate market report provides an insight into what happened on the real estate market of the past year in the state of Brandenburg. This annual report is based on statistical evaluations of sales contracts registered with the regional expert committees. Graphical representations of sales, price levels and price developments of the previous years enable a high degree of clarity of the market development. The publication of "other data necessary for the valuation", such as the real estate interest rates, provides information on value relationships and increases the transparency of the land market in the state of Brandenburg. The property market reports of previous vintages must be ordered by e-mail or fax. Note: The land market reports of the regional expert committees in analogous form can be obtained from the respective district or in the district-free city.

This dataset includes various economic indicators such as stock market performance, inflation rates, GDP, interest rates, employment data, and housing index, all of which are crucial for understanding the state of the economy. By analysing this dataset, one can gain insights into the causes and effects of past recessions in the US, which can inform investment decisions and policy-making.

There are 20 columns and 343 rows spanning 1990-04 to 2022-10

The columns are:

1. Price: Price column refers to the S&P 500 lot price over the years. The S&P 500 is a stock market index that measures the performance of 500 large companies listed on stock exchanges in the United States. This variable represents the value of the S&P 500 index from 1980 to present. Industrial Production: This variable measures the output of industrial establishments in the manufacturing, mining, and utilities sectors. It reflects the overall health of the manufacturing industry, which is a key component of the US economy.

2. INDPRO: Industrial production measures the output of the manufacturing, mining, and utility sectors of the economy. It provides insights into the overall health of the economy, as a decline in industrial production can indicate a slowdown in economic activity. This data can be used by policymakers and investors to assess the state of the economy and make informed decisions.

3. CPI: CPI stands for Consumer Price Index, which measures the change in the prices of a basket of goods and services that consumers purchase. CPI inflation represents the rate at which the prices of goods and services in the economy are increasing.

4. Treasure Bill rate (3 month to 30 Years): Treasury bills (T-bills) are short-term debt securities issued by the US government. This variable represents the interest rates on T-bills with maturities ranging from 3 months to 30 years. It reflects the cost of borrowing money for the government and provides an indication of the overall level of interest rates in the economy.

5. GDP: GDP stands for Gross Domestic Product, which is the value of all goods and services produced in a country. This dataset is taking into account only the Nominal GDP values. Nominal GDP represents the total value of goods and services produced in the US economy without accounting for inflation.

6. Rate: The Federal Funds Rate is the interest rate at which depository institutions lend reserve balances to other depository institutions overnight. It is set by the Federal Reserve and is used as a tool to regulate the money supply in the economy.

7. BBK_Index: The BBKI are maintained and produced by the Indiana Business Research Center at the Kelley School of Business at Indiana University. The BBK Coincident and Leading Indexes and Monthly GDP Growth for the U.S. are constructed from a collapsed dynamic factor analysis of a panel of 490 monthly measures of real economic activity and quarterly real GDP growth. The BBK Leading Index is the leading subcomponent of the cycle measured in standard deviation units from trend real GDP growth.

8. Housing Index: This variable represents the value of the housing market in the US. It is calculated based on the prices of homes sold in the market and provides an indication of the overall health of the housing market.

9. Recession binary column: This variable is a binary indicator that takes a value of 1 when the US economy is in a recession and 0 otherwise. It is based on the official business cycle dates provided by the National Bureau of Economic Research.

Art Presence & Property Prices in London

Quantifying the Relationship with Online Data

By [source]

About this dataset

This dataset explores the potential relationship between art presence and property prices in London neighborhoods. We conducted an analysis to investigate this by measuring the proportion of Flickr photographs with the keyword ‘art’ attached. We then compared that data to residential property price gains for each Inner London neighborhood, seeking out any associations or correlations between art presence and housing value. Our findings demonstrate the impact of aesthetics on neighborhoods, illustrating how visual environment influences socio-economic conditions. With this dataset, we aim to show how online platforms can be leveraged for quantitative data collection and analysis which can visualize these relationships so as to better understand our urban settings

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How to use the dataset

This dataset can be used to investigate the relationship between art presence and property prices in London neighborhoods. The dataset includes three columns – Postcode.District, Rank.Mean.Change, and Proportion.Art.Photos – which provide quantitative analyses of the association between art presence and price gains for London neighborhoods.

To use this dataset, first identify the postcode district for which you wish to access data by referencing a street list or PostCodeSearcher website that outlines postcodes for each neighborhood in London(http://postcodesearcher.com/london). This will allow you to easily find properties within each neighborhood as there are specific postcode districts that demarcate boundaries of particular areas (for example W2 covers Bayswater).

Once you have identified a postcode district of interest, review the ‘Rank.Mean Change’ column to explore how residential property prices have changed relative to other areas in Inner London since 2010-13 using fractions (1 = highest gain; 25 = lowest gain). Focusing on one particular location will also provide an idea about their current pricing level compared with others in order to evaluate whether further investment is worthwhile or not based on its past history of growth rates . It is important to note that higher rank numbers indicate higher price gains while lower rank numbers indicate lower price gains relative with respect from 2010-13 timeframe therefore comparing these values across many neighborhoods gives an indication as what area offers more value growth wise over given time period..

Finally pay attention how much did art contributes as far change in property price goes? To answer this question , review ‘Proportion Art Photos’ column which provides ratio of Flickr photographs associated with keyword 'art' attached within given regions helps identify visual characteristics within different localities.. Comparing proportions across various locations provide detail information regarding how much did share visual aesthetic characterstics impacts change in pricings accross different region.. For example it can give us further understandings if majority photographs are made up of urban landscape , abstracts or simply portrait presences had any role play when we look at relativity gains over past few years? Such comparisons help inform our understanding about potential impact art presence can have on changes stay relatively stable even during volatile market times..

By combining this data with other datasets related to demographics, infrastructure and socioeconomics present within londons different areas we can gain further insight which then allows us making informed decisions when it comes investing particular locations .

Research Ideas

• Use this dataset to develop a predictive analytics model to identify areas in London most likely to experience an increase in residential property prices associated with the presence of art.
• Use this dataset to develop strategies and policies that promote both artistic expression and urban development in Inner London neighborhoods.
• Compare the presence of art across inner London boroughs, as well as against other cities, to gain insight into the socio-economic conditions related to the visual environment of a city and its impact on life quality for citizens

Acknowledgements

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

License

**License: [CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication](https://creativecommons.org/publicd...

Operating-Income 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

Dataset Description

This dataset contains information about real estate listings, including various features of properties and their surrounding areas. The data can be used for analysis, prediction, and insights into the real estate market.

Features

• airports_nearest: Distance to the nearest airport in meters
• balcony: Number of balconies
• ceiling_height: Ceiling height in meters
• cityCenters_nearest: Distance to the city center in meters
• days_exposition: Number of days the listing was active (from publication to removal)
• first_day_exposition: Publication date
• floor: Floor number of the property
• floors_total: Total number of floors in the building
• is_apartment: Boolean indicating if the property is an apartment
• kitchen_area: Kitchen area in square meters
• last_price: Price at the time of listing removal
• living_area: Living area in square meters
• locality_name: Name of the locality
• open_plan: Boolean indicating if the property has an open floor plan
• parks_around3000: Number of parks within a 3 km radius
• parks_nearest: Distance to the nearest park in meters
• ponds_around3000: Number of ponds/water bodies within a 3 km radius
• ponds_nearest: Distance to the nearest pond/water body in meters
• rooms: Number of rooms
• studio: Boolean indicating if the property is a studio apartment
• total_area: Total area of the property in square meters
• total_images: Number of photos in the listing

Additional Economic Data

1. Currency Exchange Rate:

   • USD to RUB exchange rate for the period 2014-2019. This data allows for analysis of how currency fluctuations impact the real estate market.

2. Central Bank of Russia Data:

   • Key interest rate: The central bank's key policy rate, which influences lending rates and overall economic conditions.
   • Inflation rate: The rate of price increases in the economy, which can affect real estate values and investment decisions.

Potential Use Cases

1. Price Prediction: Develop machine learning models to predict property prices based on various features, including economic indicators.
2. Market Trend Analysis: Analyze how different factors, including macroeconomic variables, influence property values over time.
3. Geographical Insights: Study how location and proximity to amenities affect property desirability and pricing.
4. Listing Optimization: Investigate the relationship between listing characteristics (e.g., number of images) and time-to-sell.
5. Property Type Comparison: Compare different types of properties (e.g., studios vs. multi-room apartments) in terms of pricing and demand.
6. Urban Planning: Analyze the impact of parks, water bodies, and other urban features on property values.
7. Temporal Analysis: Study seasonal trends and long-term changes in the real estate market, considering economic factors.
8. Economic Impact Analysis: Investigate how changes in currency exchange rates, interest rates, and inflation affect the real estate market.
9. Investment Strategy: Develop models to identify optimal times for real estate investment based on economic indicators.
10. Risk Assessment: Analyze the volatility of the real estate market in relation to economic factors.

This rich dataset provides ample opportunities for both beginner and advanced data scientists to explore various aspects of the Russian real estate market. It allows for:

• Practice in data cleaning and preprocessing, especially in handling time series data
• Feature engineering, including creating derived features from economic indicators
• Applying different machine learning algorithms for regression and classification tasks
• Conducting time series analysis and forecasting
• Performing multivariate analysis to understand the complex relationships between property characteristics, location features, and economic factors

Researchers and analysts can use this dataset to gain insights into the dynamics of the Russian real estate market, understand the impact of macroeconomic factors on property values, and develop sophisticated models for price prediction and market analysis.

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.

Begin-Period-Cashflow 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

Pretax-Margin 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

From 1 April 2018, LTT replaced Stamp Duty Land Tax (SDLT) on residential and non-residential property and land interests purchased in Wales. The tax rates and tax bands for LTT vary depending on the type of transaction. Taxpayers must notify the WRA of all land transactions with a value above £40,000. There are also circumstances where certain lease transactions are not notifiable if they are less than 7 years in duration. When filing an LTT return, the organisation paying the return has 30 days after the effective date to submit and pay the return. This dataset includes estimates of LTT notifiable transactions received by the WRA by the close of 21 July 2025. Care should be taken with any comparisons over time which involve data from spring 2020 to summer 2021. This is due to the coronavirus (COVID-19) pandemic and changes to LTT rates. A national lockdown on 23 March 2020 resulted in the housing market being mainly closed from this date until 22 June 2020 when it partially re-opened. The market was re-opened more fully on 27 July, to coincide with a change in LTT rates effective until 30 June 2021. There is evidence some purchasers may have brought their transactions forward to June 2021 to benefit from the temporary tax reduction. There were some changes to LTT rates effective from 22 December 2020. Non-residential transactions and higher rates residential transactions were affected. The main residential rates and bands for Land Transaction changed for transactions effective after 10 October 2022. The dataset focuses on the transactions subject to a relief only and includes a breakdown by: - relief type: the four main categories of relief plus an ‘other’ category making up the rest - transaction type: residential, non-residential - transaction description: conveyance / transfer of ownership, granting a new lease, assignment of lease - impact on tax due: yes, no - measure: number of transactions, estimates of the value of tax relieved due, and the tax due on the transactions - effective quarter and year Reliefs can be claimed on both residential and non-residential properties. Reliefs reduce the amount of tax due when certain conditions are met. Multiple reliefs can be applied to a single transaction and reliefs may reduce the tax due to zero (known as a full relief) or by a certain percentage or amount (known as a partial relief). Reliefs are sometimes claimed where they have no impact on the tax due. These can be viewed separately in this dataset and many of them have been reported unnecessarily by the organisations completing the tax returns. As an example, some of these apply to low value residential transactions. Indications are that they are due to a perceived but mistaken need to claim first time buyer relief (which applies for the predecessor tax, but not to Land Transaction Tax). This is known following queries raised with several agents asking why tax reliefs have been claimed where there is no impact on value of the tax. Further information about this category of reliefs is provided in the example 4 in the key quality information found in the weblinks. That example also describes some adjustments that have been made to more correctly identify the value of tax relieved associated with these transactions. Further adjustments are expected in future, and so the numbers shown here for reliefs where there is no impact on tax due are likely to be revised in future. On 7 February 2025, legislation relating to multiple dwellings relief was changed so that cases where dwellings that are subsidiary (worth less than a third of the total value of the transaction) must now be treated as part of the primary dwelling in any main rates residential transaction. This means it is now very rare that multiple dwellings relief will apply in a main rates residential transaction, which we estimate will reduce the value of the relief (or increase total LTT revenues) by between £2.0 and £2.5m per year.

Abstract This paper presents the more commonly used methods of hedonic estimation and applies them to a fiscal data base from Belo Horizonte, Brazil, 1995-2012. The results of the price indexes from the various estimations suggest a pronounced real estate appreciation from 2005 to 2012. Increasing real estate financing (after the statutory lien legislation of 2004), falling interest rates and growth in real family income contributed to the positive performance of the real estate market in the period. Results also indicate that real estate prices continued to increase despite the world financial crisis of 2008/2009 possibly due to countercyclical policies implemented by the Brazilian Government. These results help to shed light on the potential use of hedonic price models and a fiscal database to construct an official housing price index for Brazil in the future.

The benchmark interest rate in Canada was last recorded at 2.25 percent. This dataset provides - Canada Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Mortgage Rate: Average: First Time Buyer: Gansu: Lanzhou data was reported at 5.280 % pa in Jun 2021. This records an increase from the previous number of 5.260 % pa for May 2021. Mortgage Rate: Average: First Time Buyer: Gansu: Lanzhou data is updated monthly, averaging 5.260 % pa from Apr 2021 (Median) to Jun 2021, with 3 observations. The data reached an all-time high of 5.280 % pa in Jun 2021 and a record low of 5.260 % pa in May 2021. Mortgage Rate: Average: First Time Buyer: Gansu: Lanzhou data remains active status in CEIC and is reported by Rong 360 BigData Research Institute. The data is categorized under China Premium Database’s Money Market, Interest Rate, Yield and Exchange Rate – Table CN.MA: Rediscount and Lending Rate: Mortgage. contact us
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The benchmark interest rate in Hong Kong was last recorded at 4.25 percent. This dataset provides the latest reported value for - Hong Kong Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus 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.

The benchmark interest rate in Netherlands was last recorded at 4.50 percent. This dataset provides - Netherlands Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.