Graph and download economic data for Housing Inventory: Median Days on Market Year-Over-Year in Boston-Cambridge-Newton, MA-NH (CBSA) (MEDDAYONMARYY14460) from Jul 2017 to May 2025 about Boston, NH, MA, median, and USA.

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

House prices grew year-on-year in most states in the U.S. in the third quarter of 2024. The District of Columbia was the only exception, with a decline of ***** percent. The annual appreciation for single-family housing in the U.S. was **** percent, while in Hawaii—the state where homes appreciated the most—the increase exceeded ** percent. How have home prices developed in recent years? House price growth in the U.S. has been going strong for years. In 2024, the median sales price of a single-family home exceeded ******* U.S. dollars, up from ******* U.S. dollars five years ago. One of the factors driving house prices was the cost of credit. The record-low federal funds effective rate allowed mortgage lenders to set mortgage interest rates as low as *** percent. With interest rates on the rise, home buying has also slowed, causing fluctuations in house prices. Why are house prices growing? Many markets in the U.S. are overheated because supply has not been able to keep up with demand. How many homes enter the housing market depends on the construction output, whereas the availability of existing homes for purchase depends on many other factors, such as the willingness of owners to sell. Furthermore, growing investor appetite in the housing sector means that prospective homebuyers have some extra competition to worry about. In certain metros, for example, the share of homes bought by investors exceeded ** percent in 2024.

The S&P Case Shiller Boston Home Price Index has risen steadily since February 2020. The index measures changes in the prices of existing single-family homes. The index value was equal to 100 as of January 2000, so if the index value is equal to 130 in a given month, for example, it means that the house prices have increased by 30 percent since 2000. The value of the S&P Case Shiller Boston Home Price Index amounted to nearly 335.36 in August 2024. That was above the national average.

Graph and download economic data for Housing Inventory: Median Days on Market Year-Over-Year in Essex County, MA (MEDDAYONMARYY25009) from Jul 2017 to May 2025 about Essex County, MA; Boston; MA; median; and USA.

The U.S. housing market has slowed, after ** consecutive years of rising home prices. In 2021, house prices surged by an unprecedented ** percent, marking the highest increase on record. However, the market has since cooled, with the Freddie Mac House Price Index showing more modest growth between 2022 and 2024. In 2024, home prices increased by *** percent. That was lower than the long-term average of *** percent since 1990. Impact of mortgage rates on homebuying The recent cooling in the housing market can be partly attributed to rising mortgage rates. After reaching a record low of **** percent in 2021, the average annual rate on a 30-year fixed-rate mortgage more than doubled in 2023. This significant increase has made homeownership less affordable for many potential buyers, contributing to a substantial decline in home sales. Despite these challenges, forecasts suggest a potential recovery in the coming years. How much does it cost to buy a house in the U.S.? In 2023, the median sales price of an existing single-family home reached a record high of over ******* U.S. dollars. Newly built homes were even pricier, despite a slight decline in the median sales price in 2023. Naturally, home prices continue to vary significantly across the country, with West Virginia being the most affordable state for homebuyers.

Only a small fraction of vacant housing units are actually considered available. Only vacant units for rent or for sale make up the available housing stock. Vacant housing that is not on the market, such as homes for seasonal, recreational, or occasional use & housing for migrant workers, are not part of the available housing stock.The housing availability rate is an indicator that economists and housing policy analysts often track. A low housing availability rate indicates a "tight" housing market (a seller's market or landlord's market) whereas a high housing availability rate indicates a buyer's or renter's market.This map shows the housing availability rate depicted by the color: pink indicates a low housing availability rate, and green indicates a high housing availability rate. The count of available housing units is depicted by the size of the symbol.This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

Housing Inventory: Median Days on Market in Middlesex County, MA was 20.00000 Level in April of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Median Days on Market in Middlesex County, MA reached a record high of 78.50000 in January of 2020 and a record low of 10.00000 in March of 2022. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Median Days on Market in Middlesex County, MA - last updated from the United States Federal Reserve on May of 2025.

Global Precast in Residential and Mass Housing market size 2025 is $158.2 Billion whereas according out published study it will reach to $265.78 Billion by 2033. Precast in Residential and Mass Housing market will be growing at a CAGR of 6.7% during 2025 to 2033.

Replication files for “Housing Market Connectedness and Transmission of Monetary Policy,” Woo Suk Lee and Eunseong Ma, Economic InquiryOverview The code in this replication package constructs the analysis file for Housing Market Connectedness and Transmission of Monetary Policy using R, EViews and Matlab. Various files are provided to generate the data for the 13 figures and 1 tables in the paper.

DisclaimerBefore using this layer, please review the 2018 Rochester Citywide Housing Market Study for the full background and context that is required for interpreting and portraying this data. Please click here to access the study. Please also note that the housing market typologies were based on analysis of property data from 2008 to 2018, and is a snapshot of market conditions within that time frame. For an accurate depiction of current housing market typologies, this analysis would need to be redone with the latest available data.About the DataThis is a webmap of a polygon feature layer containing the boundaries of all census blockgroups in the city of Rochester. Beyond the unique identifier fields including GEOID, the only other field is the housing market typology for that blockgroup. The map is visualized based on market typology score with strongest market in pink, and weakest market in dark blue.Information from the 2018 Housing Market Study- Housing Market TypologiesThe City of Rochester commissioned a Citywide Housing Market Study in 2018 as a technical study to help inform development of the City's new Comprehensive Plan, Rochester 2034 , and retained czb, LLC - a firm with national expertise based in Alexandria, VA - to perform the analysis.Any understanding of Rochester’s housing market – and any attempt to develop strategies to influence the market in ways likely to achieve community goals – must begin with recognition that market conditions in the city are highly uneven. On some blocks, competition for real estate is strong and expressed by pricing and investment levels that are above city averages. On other blocks, private demand is much lower and expressed by above average levels of disinvestment and physical distress. Still other blocks are in the middle – both in terms of condition of housing and prevailing prices. These block-by-block differences are obvious to most residents and shape their options, preferences, and actions as property owners and renters. And, importantly, these differences shape the opportunities and challenges that exist in each neighborhood, the types of policy and investment tools to utilize in response to specific needs, and the level and range of available resources, both public and private, to meet those needs. The City of Rochester has long appreciated that a one-size-fits-all approach to housing and neighborhood strategy is inadequate in such a diverse market environment, and that is no less true today. To concisely describe distinct market conditions and trends across the city in this study, a Housing Market Typology was developed using a wide range of indicators to gauge market health and investment behaviors. This section of the Citywide Housing Market Study introduces the typology and its components. In later sections, the typology is used as a tool for describing and understanding demographic and economic patterns within the city, the implications of existing market patterns on strategy development, and how existing or potential policy and investment tools relate to market conditions.Overview of Housing Market Typology PurposeThe Housing Market Typology in this study is a tool for understanding recent market conditions and variations within Rochester and informing housing and neighborhood strategy development. As with any typology, it is meant to simplify complex information into a limited number of meaningful categories to guide action. Local context and knowledge remain critical to understanding market conditions and should always be used alongside the typology to maximize its usefulness.Geographic Unit of Analysis The Block Group – a geographic unit determined by the U.S. Census Bureau – is the unit of analysis for this typology, which utilizes parcel-level data. There are over 200 Block Groups in Rochester, most of which cover a small cluster of city blocks and are home to between 600 and 3,000 residents. For this tool, the Block Group provides geographies large enough to have sufficient data to analyze and small enough to reveal market variations within small areas.Four Components for CalculationAnalysis of multiple datasets led to the identification of four typology components that were most helpful in drawing out market variations within the city:• Terms of Sale• Market Strength• Bank Foreclosures• Property DistressThose components are described one-by-one on in the full study document (LINK), with detailed methodological descriptions provided in the Appendix.A Spectrum of Demand The four components were folded together to create the Housing Market Typology. The seven categories of the typology describe a spectrum of housing demand – with lower scores indicating higher levels of demand, and higher scores indicating weaker levels of demand. Typology 1 are areas with the highest demand and strongest market, while typology 3 are the weakest markets. For more information please visit: https://www.cityofrochester.gov/HousingMarketStudy2018/

Graph and download economic data for Housing Inventory: Median Days on Market Month-Over-Month in Pittsfield, MA (CBSA) (MEDDAYONMARMM38340) from Jul 2017 to May 2025 about Pittsfield, MA, median, and USA.

China Residential Real Estate Market size was valued at USD 570000 Million in 2023 and is projected to reach USD 792166 Million by 2031, growing at a CAGR of 4.2% during the forecast period 2024-2031.

China Residential Real Estate Market: Definition/ Overview

Residential real estate refers to properties designed specifically for individuals or families to live in, offering a broad spectrum of housing options that cater to various lifestyles, preferences, and financial situations. This category includes several types of properties, each with distinct advantages based on size, location, and amenities. Condominiums (Condos) are individual units within larger buildings or complexes, often offering shared amenities like gyms, pools, and communal spaces, making them ideal for those seeking a low-maintenance, urban lifestyle.

Townhouses are multi-level homes attached to neighboring units, providing the benefits of both space and privacy while maintaining a compact footprint, often with more room than apartments but less than detached homes.

Housing Inventory: Median Days on Market Year-Over-Year in Norfolk County, MA was -3.45% in April of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Median Days on Market Year-Over-Year in Norfolk County, MA reached a record high of 150.00 in April of 2023 and a record low of -68.52 in May of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Median Days on Market Year-Over-Year in Norfolk County, MA - last updated from the United States Federal Reserve on May of 2025.

Housing Inventory: Median Days on Market Month-Over-Month in Plymouth County, MA was 17.65% in May of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Median Days on Market Month-Over-Month in Plymouth County, MA reached a record high of 46.00 in July of 2024 and a record low of -48.00 in February of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Median Days on Market Month-Over-Month in Plymouth County, MA - last updated from the United States Federal Reserve on June of 2025.

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

A. Usecase/Applications possible with the data:

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

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

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

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

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

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

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

How does it work?

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

Graph and download economic data for Housing Inventory: Median Listing Price in Massachusetts (MEDLISPRIMA) from Jul 2016 to May 2025 about MA, listing, median, price, and USA.

Severen, C. (2023). “Commuting, Labor, and Housing Market Effects of Mass Transportation: Welfare and Identification.” Review of Economics and Statistics 105:5, 1073–1091.

Mapping spatial processes at a small scale is a challenge when observed data are not abundant. The article examines the residential housing market in Fort Worth, Texas, and builds price indices at the inter- and intra-neighborhood levels. To accomplish our objectives, we initially model price variability in the joint space–time continuum. We then use geostatistics to predict and map monthly housing prices across the area of interest over a period of 4 years. For this analysis, we introduce the Bayesian maximum entropy (BME) method into real estate research. We use BME because it rigorously integrates uncertain or secondary soft data, which are needed to build the price indices. The soft data in our analysis are property tax values, which are plentiful, publicly available, and highly correlated with transaction prices. The results demonstrate how the use of the soft data provides the ability to map house prices within a small areal unit such as a subdivision or neighborhood.

In 2022, San Jose, CA, was the hottest market for millennial homebuyers in the United States. Millennials in San Jose were responsible for nearly 64 percent of the house purchase requests. Denver, CO, and Boston, MA, completed the top three with over 60 percent of purchase requests. Which are the states with the youngest population in the U.S.? It should come as no surprise that the demographic composition plays a central role in the development of the housing market in different states. In 2020, the median age in the United States was 38.2 years, but some states, such as Alaska, District of Columbia, and Utah had much younger population. In contrast, Maine, Puerto Rico, and Hampshire had the highest median age of population. Millennials’ attitudes towards homeownership While many millennials have given up on homeownership, one in three people share that they are in the process of saving for a home purchase. These results suggest that young Americans have not entirely given up on the American dream of owning a home of their own.