Graph and download economic data for All-Transactions House Price Index for the Pacific Census Division (CPACSTHPI) from Q1 1975 to Q1 2025 about Pacific Census Division, appraisers, HPI, housing, price index, indexes, price, and USA.

Graph and download economic data for Purchase Only House Price Index for the Pacific Census Division (PONHPI00109M226N) from Jan 1991 to Apr 2025 about Pacific Census Division, HPI, housing, price index, indexes, price, and USA.

United States House Price Index: FHFA: Expanded: Pacific data was reported at 298.040 Jan1991=100 in Mar 2018. This records an increase from the previous number of 292.740 Jan1991=100 for Dec 2017. United States House Price Index: FHFA: Expanded: Pacific data is updated quarterly, averaging 170.380 Jan1991=100 from Mar 1991 (Median) to Mar 2018, with 109 observations. The data reached an all-time high of 298.040 Jan1991=100 in Mar 2018 and a record low of 96.840 Jan1991=100 in Mar 1994. United States House Price Index: FHFA: Expanded: Pacific data remains active status in CEIC and is reported by Federal Housing Finance Agency. The data is categorized under Global Database’s USA – Table US.EB015: House Price Index: Expanded.

United States House Price Index: FHFA: Purchase Only: sa: Pacific data was reported at 306.700 Jan1991=100 in May 2018. This records an increase from the previous number of 306.200 Jan1991=100 for Apr 2018. United States House Price Index: FHFA: Purchase Only: sa: Pacific data is updated monthly, averaging 176.240 Jan1991=100 from Jan 1991 (Median) to May 2018, with 329 observations. The data reached an all-time high of 306.700 Jan1991=100 in May 2018 and a record low of 95.000 Jan1991=100 in Apr 1995. United States House Price Index: FHFA: Purchase Only: sa: Pacific data remains active status in CEIC and is reported by Federal Housing Finance Agency. The data is categorized under Global Database’s USA – Table US.EB014: House Price Index.

The year-end value of the S&P Case Shiller National Home Price Index amounted to 321.45 in 2024. The index value was equal to 100 as of January 2000, so if the index value is equal to 130 in a given year, for example, it means that the house prices increased by 30 percent since 2000. S&P/Case Shiller U.S. home indices – additional informationThe S&P Case Shiller National Home Price Index is calculated on a monthly basis and is based on the prices of single-family homes in nine U.S. Census divisions: New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, Mountain and Pacific. The index is the leading indicator of the American housing market and one of the indicators of the state of the broader economy. The index illustrates the trend of home prices and can be helpful during house purchase decisions. When house prices are rising, a house buyer might want to speed up the house purchase decision as the transaction costs can be much higher in the future. The S&P Case Shiller National Home Price Index has been on the rise since 2011.The S&P Case Shiller National Home Price Index is one of the indices included in the S&P/Case-Shiller Home Price Index Series. Other indices are the S&P/Case Shiller 20-City Composite Home Price Index, the S&P/Case Shiller 10-City Composite Home Price Index and twenty city composite indices.

pacific census division - All-Transactions House Price Index for the Pacific Census Division was 981.42000 Index 1980 Q1=100 in January of 2025, according to the United States Federal Reserve. Historically, pacific census division - All-Transactions House Price Index for the Pacific Census Division reached a record high of 981.42000 in January of 2025 and a record low of 43.69000 in January of 1975. Trading Economics provides the current actual value, an historical data chart and related indicators for pacific census division - All-Transactions House Price Index for the Pacific Census Division - last updated from the United States Federal Reserve on June of 2025.

All-Transactions House Price Index for Pacific County, WA was 299.43000 Index 2000=100 in January of 2024, according to the United States Federal Reserve. Historically, All-Transactions House Price Index for Pacific County, WA reached a record high of 299.43000 in January of 2024 and a record low of 68.61000 in January of 1992. Trading Economics provides the current actual value, an historical data chart and related indicators for All-Transactions House Price Index for Pacific County, WA - last updated from the United States Federal Reserve on July of 2025.

United States House Price Index: FHFA: Pacific data was reported at 652.120 Mar1980=100 in Jun 2018. This records an increase from the previous number of 633.670 Mar1980=100 for Mar 2018. United States House Price Index: FHFA: Pacific data is updated quarterly, averaging 215.320 Mar1980=100 from Mar 1975 (Median) to Jun 2018, with 174 observations. The data reached an all-time high of 652.120 Mar1980=100 in Jun 2018 and a record low of 43.460 Mar1980=100 in Mar 1975. United States House Price Index: FHFA: Pacific data remains active status in CEIC and is reported by Federal Housing Finance Agency. The data is categorized under Global Database’s USA – Table US.EB014: House Price Index.

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 *** index points. This means that, adjusted for inflation, house prices grew ** 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.

Graph and download economic data for Purchase Only House Price Index for the Pacific Census Division (PONHPI10900Q226N) from Q1 1991 to Q1 2025 about Pacific Census Division, HPI, housing, price index, indexes, price, and USA.

pacific census division - Purchase Only House Price Index for the Pacific Census Division was 462.28000 Index 1991 Q1=100 in October of 2024, according to the United States Federal Reserve. Historically, pacific census division - Purchase Only House Price Index for the Pacific Census Division reached a record high of 468.52000 in April of 2024 and a record low of 95.32000 in October of 1995. Trading Economics provides the current actual value, an historical data chart and related indicators for pacific census division - Purchase Only House Price Index for the Pacific Census Division - last updated from the United States Federal Reserve on July of 2025.

pacific census division - Purchase Only House Price Index for the Pacific Census Division was 472.00000 Index Jan 1991=100 in February of 2025, according to the United States Federal Reserve. Historically, pacific census division - Purchase Only House Price Index for the Pacific Census Division reached a record high of 475.66000 in January of 2025 and a record low of 95.19000 in April of 1995. Trading Economics provides the current actual value, an historical data chart and related indicators for pacific census division - Purchase Only House Price Index for the Pacific Census Division - last updated from the United States Federal Reserve on May of 2025.

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 331 index points. This suggests an increase of 231 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.

In the fourth quarter (Q4) of 2024, the Philippines exhibited the highest year-on-year (YoY) inflation-adjusted increase in house prices among the Asia-Pacific countries under observation. In Q4 2024, real house prices in the Philippines grew by about *** percent YoY, while in Hong Kong, prices declined by about *** percent YoY over the same period. These figures are based on the development of the real house price index, using 2010 as a baseline year.

In 2024, Australia was the country with the highest nominal increase in house prices in the Asia-Pacific (APAC region). In the first quarter of the year, house prices in Australia rose by nearly 10 percent annually, whereas globally, the increase was three percent. These figures are based on the development of the nominal house price index, with 2010 chosen as a baseline year. Furthermore, it is important to note that the nominal index does not account for the effects of inflation, meaning that price growth in real terms was slower.

About this dataset

Context

The dataset illustrates the median household income in Pacific, spanning the years from 2010 to 2021, with all figures adjusted to 2022 inflation-adjusted dollars. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2021, the median household income for Pacific decreased by $9,865 (16.05%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $4,559 (6.51%) between 2010 and 2021.

Analyzing the trend in median household income between the years 2010 and 2021, spanning 11 annual cycles, we observed that median household income, when adjusted for 2022 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 4 years and declined for 7 years.

https://i.neilsberg.com/ch/pacific-mo-median-household-income-trend.jpeg" alt="Pacific, MO median household income trend (2010-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2021
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2022 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Pacific median household income. You can refer the same here

Bureau of Labor Statistics Pacific Rim - Import Price Index by Origin (NAICS): Household and Institutional Furniture and Kitchen Cabinet Manufacturing for Pacific Rim was 100.20000 Index 2010=100 in April of 2025, according to the United States Federal Reserve. Historically, Bureau of Labor Statistics Pacific Rim - Import Price Index by Origin (NAICS): Household and Institutional Furniture and Kitchen Cabinet Manufacturing for Pacific Rim reached a record high of 106.60000 in April of 2022 and a record low of 98.80000 in September of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Bureau of Labor Statistics Pacific Rim - Import Price Index by Origin (NAICS): Household and Institutional Furniture and Kitchen Cabinet Manufacturing for Pacific Rim - last updated from the United States Federal Reserve on June of 2025.

Graph and download economic data for Import Price Index by Origin (NAICS): Household Appliance Manufacturing for Pacific Rim (COPRIMZ3352) from Jun 2012 to May 2025 about appliances, imports, households, manufacturing, price index, indexes, and price.

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