This dataset provides values for FHFA HOUSE PRICE INDEX MOM reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The average for 2021 based on 10 countries was 49.666 index points. The highest value was in Singapore: 135 index points and the lowest value was in Thailand: 25.94 index points. The indicator is available from 2017 to 2021. Below is a chart for all countries where data are available.

As of the fourth quarter of 2024, Malaysia was the country with the highest inflation-adjusted increase in house prices since 2010 among the Asia-Pacific (APAC) countries under observation. The real house price index in Malaysia reached nearly 167 index points. This means that, adjusted for inflation, house prices grew 67 percent since 2010, the baseline year when the index value was set to 100. According to the nominal house price index, which does not adjust for the effects of inflation, the price increase was higher.

In 2024, India was the country with the highest increase in house prices since 2010 among the Asia-Pacific (APAC) countries under observation. In the first quarter of the year, the nominal house price index in India reached almost *** index points. This suggests an increase of *** percent since 2010, the baseline year when the index value was set to 100. It is important to note that the nominal index does not account for the effects of inflation, meaning when adjusted for inflation, price growth in real terms was slower.

To investigate the issue of inflation-hedging to find appropriate hedging assets against inflation by using the VAR or VECM model. We have collected data encompassing housing price indices, stock indices, price indexes, and money supply from five countries: the United States, Hong Kong, South Korea, Singapore, and Taiwan. The housing price index focuses on the transaction prices of listed residential houses in the metropolitan area as the benchmark, the stock price index is the ordinary stock market index of various countries, the price index is the consumer price index (CPI), and the money supply is M2 aggregate. The time period for obtaining data on the housing price index and stock price index is not the same.

The average for 2021 based on 40 countries was 62.567 index points. The highest value was in Israel: 268.76 index points and the lowest value was in Tajikistan: 12.89 index points. The indicator is available from 2017 to 2021. Below is a chart for all countries where data are available.

AI-Powered Rental Price Index Market Outlook

According to our latest research, the global AI-Powered Rental Price Index market size reached USD 1.84 billion in 2024, with a robust compound annual growth rate (CAGR) of 17.2% projected through the forecast period. By 2033, the market is anticipated to achieve a value of USD 8.19 billion, driven by increasing demand for data-driven pricing strategies, rapid digital transformation in real estate, and the growing adoption of artificial intelligence across property valuation and management. As per our comprehensive analysis, the market is witnessing exponential growth due to the need for accurate, real-time rental price insights, supporting both property owners and tenants in making informed decisions.

One of the primary growth factors fueling the AI-Powered Rental Price Index market is the escalating need for transparency and precision in rental pricing, especially in highly dynamic urban real estate environments. Traditional pricing methodologies often fall short in accounting for rapidly shifting market variables, such as sudden changes in demand, local economic trends, or emerging neighborhood developments. AI-powered solutions leverage advanced algorithms and machine learning models to process vast datasets, including historical rental prices, property attributes, neighborhood analytics, and even social sentiment. This enables real estate stakeholders to arrive at more accurate and competitive rental prices, minimizing vacancies and maximizing returns. Further, the integration of AI with Internet of Things (IoT) and smart city initiatives is enhancing the granularity and timeliness of rental data, solidifying the value proposition of AI-powered rental indices.

Another significant growth driver is the increasing adoption of digital platforms by real estate agencies, property managers, and institutional investors. The transformation from manual, spreadsheet-based assessments to automated, AI-driven platforms is streamlining operations, reducing human error, and enabling scalable portfolio management. Financial institutions are also leveraging AI-powered rental indices for risk assessment, loan underwriting, and investment analysis, further expanding the addressable market. Additionally, the proliferation of proptech startups and increased venture capital investments in real estate technology are accelerating the innovation cycle, resulting in more sophisticated and customizable AI-powered pricing solutions. The rising consumer expectation for transparency and fairness in rental pricing, particularly among younger, tech-savvy renters, is further catalyzing market growth.

Furthermore, regulatory developments and government initiatives aimed at improving housing affordability and market efficiency are positively impacting the AI-Powered Rental Price Index market. In many regions, public sector agencies are collaborating with technology providers to develop standardized rental indices, which support policy-making, rent control measures, and urban planning. These collaborations are fostering an environment where AI-powered analytics are not only a competitive advantage for private enterprises but also a tool for public good. However, market expansion is somewhat tempered by challenges related to data privacy, algorithmic transparency, and the need for standardized data formats across jurisdictions. Addressing these issues will be crucial for sustained growth and broader adoption in the coming years.

Regionally, North America continues to dominate the AI-Powered Rental Price Index market, accounting for the largest share in 2024, owing to its mature real estate sector, high digital adoption, and strong presence of leading proptech firms. Europe is experiencing rapid growth, particularly in countries with high urbanization rates and regulatory support for digital transformation in real estate. Asia Pacific is emerging as a high-growth region, driven by urban expansion, smart city projects, and a burgeoning middle class seeking reliable rental information. While Latin America and Middle East & Africa are currently smaller markets, they present significant long-term potential as digital infrastructure and real estate investment accelerate. Overall, regional dynamics are shaped by varying levels of technological maturity, regulatory frameworks, and the pace of urbanization.

AI-Powered Rental Price Index Market Outlook

According to our latest research, the AI-Powered Rental Price Index market size reached USD 1.7 billion in 2024, reflecting the rapid adoption of artificial intelligence technologies in the real estate sector. The market is projected to grow at a robust CAGR of 18.9% from 2025 to 2033, with the forecasted market size anticipated to reach USD 8.5 billion by 2033. This impressive growth trajectory is driven by the increasing demand for data-driven rental pricing solutions, the proliferation of smart property management systems, and the need for real-time market intelligence among property stakeholders.

One of the key growth factors fueling the expansion of the AI-Powered Rental Price Index market is the escalating complexity and dynamism of global rental markets. Traditional pricing models often fail to capture the nuanced shifts in demand and supply, especially in urban and high-growth regions. AI-powered solutions leverage vast datasets, including historical rental data, economic indicators, neighborhood trends, and even social sentiment, to provide highly accurate and adaptive rental price indices. This enables property managers, landlords, and real estate agencies to optimize pricing strategies, reduce vacancy rates, and maximize returns. The ability to harness predictive analytics and machine learning for rental price forecasting is increasingly seen as a competitive differentiator in the industry.

Another significant driver is the digital transformation sweeping through the real estate sector. The integration of AI-powered rental price indices with property management platforms, listing services, and financial analytics tools is streamlining operations and enhancing decision-making. Cloud-based deployment models are making these advanced analytics accessible to a broader range of users, from large real estate agencies to individual landlords. The automation of rental price assessments not only reduces human error but also accelerates the leasing process, providing a seamless experience for both property owners and tenants. Furthermore, the growing emphasis on transparency and fairness in rental pricing is prompting regulatory bodies and public sector organizations to adopt AI-driven solutions for market monitoring and policy formulation.

The surge in urbanization and the proliferation of rental properties, especially in emerging economies, are also contributing to market growth. As cities expand and rental housing becomes a primary option for a growing segment of the population, the need for accurate, real-time rental price indices becomes critical. AI-powered platforms are uniquely positioned to capture hyper-local trends, adjust for seasonality, and factor in external events such as economic shocks or policy changes. This level of granularity and agility is essential for navigating the increasingly competitive and fragmented rental market landscape. Additionally, the COVID-19 pandemic has accelerated the adoption of digital solutions in real estate, further boosting the demand for AI-powered rental price indices.

Regionally, North America currently dominates the AI-Powered Rental Price Index market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The United States, in particular, has witnessed widespread adoption of AI-driven property management tools, supported by a mature real estate ecosystem and high digital literacy. Europe is rapidly catching up, driven by regulatory initiatives and a strong focus on data-driven urban planning. The Asia Pacific region is expected to exhibit the highest CAGR over the forecast period, fueled by rapid urbanization, rising investments in proptech startups, and the digitalization of real estate services in countries like China, India, and Australia. Latin America and the Middle East & Africa are also emerging as promising markets, albeit from a smaller base, as local governments and private players recognize the value of AI in addressing housing market inefficiencies.

Component Analysis

The AI-Powered Rental Price Index market is segmented by component into Software and Services, each playing a pivotal role in the ecosystem. The software segment comprises AI algorithms, analytics engines, and user interfaces that enable stakeholders to access, interpret, and act on rental price data. These platforms are increasingly incorporating advanced features such as n

Graph and download economic data for Import Price Index: Household and institutional furniture and kitchen cabinet manufacturing for Eastern Europe, Latin America, OPEC countries, and other countries in Asia, Africa, and the Western Hemisphere (DISCONTINUED) (COOTHERZ3371) from Jun 2012 to Dec 2017 about Africa, Asia, Latin America, furniture, imports, Europe, households, manufacturing, price index, indexes, and price.

United States - Import Price Index: Household appliance manufacturing for Eastern Europe, Latin America, OPEC countries, and other countries in Asia, Africa, and the Western Hemisphere (DISCONTINUED) was 94.70000 Index 2010=100 in December of 2017, according to the United States Federal Reserve. Historically, United States - Import Price Index: Household appliance manufacturing for Eastern Europe, Latin America, OPEC countries, and other countries in Asia, Africa, and the Western Hemisphere (DISCONTINUED) reached a record high of 101.50000 in March of 2013 and a record low of 92.70000 in March of 2017. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Import Price Index: Household appliance manufacturing for Eastern Europe, Latin America, OPEC countries, and other countries in Asia, Africa, and the Western Hemisphere (DISCONTINUED) - last updated from the United States Federal Reserve on October of 2025.

United States - Import Price Index: Household and institutional furniture and kitchen cabinet manufacturing for Eastern Europe, Latin America, OPEC countries, and other countries in Asia, Africa, and the Western Hemisphere (DISCONTINUED) was 101.20000 Index 2010=100 in December of 2017, according to the United States Federal Reserve. Historically, United States - Import Price Index: Household and institutional furniture and kitchen cabinet manufacturing for Eastern Europe, Latin America, OPEC countries, and other countries in Asia, Africa, and the Western Hemisphere (DISCONTINUED) reached a record high of 102.40000 in September of 2014 and a record low of 99.40000 in July of 2012. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Import Price Index: Household and institutional furniture and kitchen cabinet manufacturing for Eastern Europe, Latin America, OPEC countries, and other countries in Asia, Africa, and the Western Hemisphere (DISCONTINUED) - last updated from the United States Federal Reserve on September of 2025.

Graph and download economic data for Import Price Index: Household appliance manufacturing for Eastern Europe, Latin America, OPEC countries, and other countries in Asia, Africa, and the Western Hemisphere (DISCONTINUED) (COOTHERZ3352) from Jun 2012 to Dec 2017 about Africa, Latin America, Asia, appliances, imports, Europe, households, manufacturing, price index, indexes, and price.

Price-To-Tangible-Book-Ratio Time Series for Mapletree Pan Asia Commercial Trust. Mapletree Pan Asia Commercial Trust ("MPACT") is a real estate investment trust ("REIT") positioned to be the proxy to key gateway markets of Asia. Listed on the Singapore Exchange Securities Limited on 27 April 2011, it made its public market debut as Mapletree Commercial Trust and was subsequently renamed MPACT on 3 August 2022 following the merger with Mapletree North Asia Commercial Trust. Its principal investment objective is to invest on a long-term basis, directly or indirectly, in a diversified portfolio of income-producing real estate used primarily for office and/or retail purposes, as well as real estate-related assets, in the key gateway markets of Asia (including but not limited to Singapore, Hong Kong, China, Japan and South Korea). MPACT's portfolio comprises 17 commercial properties across five key gateway markets of Asia " four in Singapore, one in Hong Kong, two in China, nine in Japan and one in South Korea. They have a total lettable area of 10.5 million square feet independently valued at S$16.0 billion.

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.

PropertyGuru Stock (PGRU) - Navigating the Southeast Asian Real Estate Landscape

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

This statistic shows a ranking of the estimated real per capita consumer spending on food in 2020 in Asia, differentiated by country. Consumer spending here refers to the domestic demand of private households and non-profit institutions serving households (NPISHs) in the selected region. Spending by corporations or the state is not included. Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group 01. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data has been converted from local currencies to US$ using the average constant exchange rate of the base year 2017. The timelines therefore do not incorporate currency effects. The data is shown in real terms which means that monetary data is valued at constant prices of a given base year (in this case: 2017). To attain constant prices the nominal forecast has been deflated with the projected consumer price index for the respective category.The shown forecast is adjusted for the expected impact of the COVID-19 pandemic on the local economy. The impact has been estimated by considering both direct (e.g. because of restrictions on personal movement) and indirect (e.g. because of weakened purchasing power) effects. The impact assessment is subject to periodic review as more data becomes available.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

Ebit Time Series for Link Real Estate Investment Trust. Link Real Estate Investment Trust (Link REIT) is the largest REIT in Asia by many measures including asset value. Managed by Link Asset Management Limited (Link), a leading, independent and fully-integrated real estate investor and manager focusing on the APAC region, Link REIT has been entirely owned by independent investors since its listing in November 2005 as the first REIT in Hong Kong. After initially acquiring a portfolio of shopping centres and car parks in Hong Kong valued at around HK$33 billion at the time of its IPO, Link has grown and diversified the Link REIT's property portfolio. Today, the portfolio includes retail facilities, car parks, offices, and logistics assets which span Hong Kong, Mainland China, Australia, Singapore, and the UK, with a total valuation of around HK$226 billion (As at 31 March 2025). Link aims to further grow and diversify the Link REIT portfolio to continue delivering resilient returns and growth to Unitholders. Link REIT is a constituent of the Hong Kong securities market benchmark Hang Seng Index, as well as a component of the Dow Jones Sustainability Asia Pacific Index, the FTSE4Good Index Series and the Hang Seng Corporate Sustainability Index. Asset management, portfolio management and capital management are three pillars of our management strengths. We are committed to integrating Environment, Social and Governance (ESG) considerations into our strategy and daily operations.

Stock Price Time Series for Jones Lang LaSalle Incorporated. Jones Lang LaSalle Incorporated, a commercial real estate and investment management company, engages in the buying, building, occupying, managing, and investing in commercial, industrial, hotel, residential, and retail properties in the Americas, Europe, the Middle East, Africa, and the Asia Pacific. The company also offers real estate agency leasing, tenant representation, property management, advisory, and consulting services; and debt advisory, loan sales and servicing, value and risk advisory, equity and funds placement, merger and acquisition, corporate advisory, and investment sales and advisory services. In addition, it provides on-site real estate management services for office, industrial, retail, multifamily residential, and other properties; cloud-based software solutions; integrated facilities management, space planning, office design, and workplace strategy consulting services; program and project management, implementation and support, managed services, and advisory/consulting services; and investment management services to institutional investors and high-net-worth individuals, as well as designing, building, management, and consulting services to tenants of leased space, owners in self-occupied buildings, and owners of real estate investments. It provides its services to real estate owners, occupiers, investors, and developers for various property types, including critical environments and data centers, offices, industrial and warehouses, residential properties, infrastructure projects, retail and shopping malls, logistics, and military housing and transportation centers; and hotels and hospitality, cultural, educational, government, healthcare and laboratory, and sports facilities. The company was formerly known as LaSalle Partners Incorporated and changed its name to Jones Lang LaSalle Incorporated in March 1999. Jones Lang LaSalle Incorporated was incorporated in 1997 and is headquartered in Chicago, Illinois.

Stock Price Time Series for Savills. Savills plc, together with its subsidiaries, engages in the provision of real estate services in the United Kingdom, Continental Europe, the Asia Pacific, Africa, North America, and the Middle East. The company advises on commercial, residential, rural, and leisure properties; and provides corporate finance advisory, investment management, and a range of property-related financial services. It operates through Transaction Advisory, Property and Facilities Management, Investment Management, and Consultancy segments. The Transaction Advisory segment offers commercial, residential, leisure, and agricultural leasing services; and tenant representation, as well as investment advice on purchases and sales. The Property and Facilities Management segment manages commercial, residential, leisure, and agricultural properties for owners; and provides services to occupiers of properties, including strategic advice and project management, as well as various services relating to a property. The Investment Management segment is involved in the investment management of commercial and residential property portfolios for institutional, corporate, or private investors on a pooled or segregated account basis. The Consultancy segment offers various professional property services, such as valuation, project management and housing consultancy, environmental consultancy, landlord and tenant, rating, development, planning, strategic projects, and corporate services and research. Savills plc was founded in 1855 and is headquartered in London, the United Kingdom.