The average price per square foot of floor space in new single-family housing in the United States decreased after the great financial crisis, followed by several years of stagnation. Since 2012, the price has continuously risen, hitting *** U.S. dollars per square foot in 2022. In 2024, the average sales price of a new home exceeded ******* U.S. dollars. Development of house sales in the U.S. One of the reasons for rising property prices is the gradual growth of house sales between 2011 and 2020. This period was marked by the gradual recovery following the subprime mortgage crisis and a growing housing sentiment. Another significant factor for the housing demand was the growing number of new household formations each year. Despite this trend, housing transactions plummeted in 2021, amid soaring prices and borrowing costs. In 2021, the average construction cost for single-family housing rose by nearly ** percent year-on-year, and in 2022, the increase was even higher, at close to ** percent. Financing a house purchase Mortgage interest rates in the U.S. rose dramatically in 2022 and remained elevated until 2024. In 2020, a homebuyer could lock in a 30-year fixed interest rate of under ***** percent, whereas in 2024, the average rate for the same mortgage type was more than twice higher. That has led to a decline in homebuyer sentiment, and an increasing share of the population pessimistic about buying a home in the current market.

Graph and download economic data for Housing Inventory: Median Listing Price per Square Feet in Texas (MEDLISPRIPERSQUFEETX) from Jul 2016 to Jun 2025 about square feet, listing, TX, median, price, and USA.

The average sales price of new homes in the United States experienced a slight decrease in 2024, dropping to 512,2000 U.S. dollars from the peak of 521,500 U.S. dollars in 2022. This decline came after years of substantial price increases, with the average price surpassing 400,000 U.S. dollars for the first time in 2021. The recent cooling in the housing market reflects broader economic trends and changing consumer sentiment towards homeownership. Factors influencing home prices and affordability The rapid rise in home prices over the past few years has been driven by several factors, including historically low mortgage rates and increased demand during the COVID-19 pandemic. However, the market has since slowed down, with the number of home sales declining by over two million between 2021 and 2023. This decline can be attributed to rising mortgage rates and decreased affordability. The Housing Affordability Index hit a record low of 98.1 in 2023, indicating that the median-income family could no longer afford a median-priced home. Future outlook for the housing market Despite the recent cooling, experts forecast a potential recovery in the coming years. The Freddie Mac House Price Index showed a growth of 6.5 percent in 2023, which is still above the long-term average of 4.4 percent since 1990. However, homebuyer sentiment remains low across all age groups, with people aged 45 to 64 expressing the most pessimistic outlook. The median sales price of existing homes is expected to increase slightly until 2025, suggesting that affordability challenges may persist in the near future.

South Carolina Real Estate Dataset 2025

Dataset Overview

This comprehensive real estate dataset contains over 5,000 property listings from South Carolina, collected in 2025 from Realtor.com using apify api. The dataset captures diverse property types including single-family homes, condominiums, land parcels, townhomes, and other residential properties. This dataset provides a rich snapshot of South Carolina's real estate market suitable for predictive modeling, market analysis, and investment research.

Data Science Applications

• Price Prediction Models: Build regression models (Random Forest, XGBoost, Neural Networks) to predict property values based on size, location, bedrooms, and age
• Property Type Classification: Develop multi-class classifiers to categorize properties based on physical characteristics
• Market Segmentation: Apply clustering algorithms (K-means, DBSCAN) to identify distinct property segments and price brackets
• Time Series Analysis: Analyze construction trends and property age distributions to forecast future development patterns
• Investment Opportunity Detection: Create anomaly detection models to identify undervalued properties or outliers
• Feature Engineering: Generate derived features like price per square foot, bathroom-to-bedroom ratios for enhanced model performance

Column Descriptors

• type: Primary property category (single_family, condos, land, townhomes, multi_family, farm)
• sub_type: Detailed property classification (condo, townhouse, co_op)
• sqft: Property size in square feet
• baths: Number of bathrooms (decimal values indicate half baths)
• beds: Number of bedrooms
• stories: Number of floors/stories in the property
• year_built: Construction year of the property
• listPrice: Property listing price in USD

Ethically Obtained Data

This dataset was ethically scraped from publicly available listings on Realtor.com and is provided strictly for educational and learning purposes only. The data collection complied with ethical web scraping practices and contains only publicly accessible information. Users should utilize this dataset exclusively for academic research, educational projects, and learning data science techniques. Any commercial use is strictly prohibited.

Analysis of ‘Boston House Prices-Advanced Regression Techniques’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/fedesoriano/the-boston-houseprice-data on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Similar Datasets

• Gender Pay Gap Dataset: LINK
• California Housing Prices Data (5 new features!): LINK
• Company Bankruptcy Prediction: LINK

Context

The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.

Attribute Information

Input features in order: 1) CRIM: per capita crime rate by town 2) ZN: proportion of residential land zoned for lots over 25,000 sq.ft. 3) INDUS: proportion of non-retail business acres per town 4) CHAS: Charles River dummy variable (1 if tract bounds river; 0 otherwise) 5) NOX: nitric oxides concentration (parts per 10 million) [parts/10M] 6) RM: average number of rooms per dwelling 7) AGE: proportion of owner-occupied units built prior to 1940 8) DIS: weighted distances to five Boston employment centres 9) RAD: index of accessibility to radial highways 10) TAX: full-value property-tax rate per $10,000 [$/10k] 11) PTRATIO: pupil-teacher ratio by town 12) B: The result of the equation B=1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town 13) LSTAT: % lower status of the population

Output variable: 1) MEDV: Median value of owner-occupied homes in $1000's [k$]

Source

StatLib - Carnegie Mellon University

Relevant Papers

Harrison, David & Rubinfeld, Daniel. (1978). Hedonic housing prices and the demand for clean air. Journal of Environmental Economics and Management. 5. 81-102. 10.1016/0095-0696(78)90006-2. LINK

Belsley, David A. & Kuh, Edwin. & Welsch, Roy E. (1980). Regression diagnostics: identifying influential data and sources of collinearity. New York: Wiley LINK

--- Original source retains full ownership of the source dataset ---

This dataset represents residential real estate listings with the following features:

ZIP: The ZIP code where the property is located || SOLDPRICE: The listing price of the property in USD || SQFT: The square footage (living area) of the property || LOTSIZE: The size of the lot (in square feet) on which the property is located || BED: The number of bedrooms in the property || BATH: The number of bathrooms (some may include half baths as "0.5") || AGE: The year the property was built (older properties have lower numbers, indicating their age) || DOM: The number of days the property has been on the market (a negative value might indicate a correction or pending listing) ||

Each row represents an individual property, and the values provide various characteristics of that listing, such as size, price, and how long it's been available for purchase.

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Concerns housing values in suburbs of Boston.

1. Number of Instances: 506

2. Number of Attributes: 13 continuous attributes (including "class" attribute "MEDV"), 1 binary-valued attribute.

3. Attribute Information:

   1. CRIM per capita crime rate by town
   2. ZN proportion of residential land zoned for lots over 25,000 sq.ft.
   3. INDUS proportion of non-retail business acres per town
   4. CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
   5. NOX nitric oxides concentration (parts per 10 million)
   6. RM average number of rooms per dwelling
   7. AGE proportion of owner-occupied units built prior to 1940
   8. DIS weighted distances to five Boston employment centres
   9. RAD index of accessibility to radial highways
   10. TAX full-value property-tax rate per $10,000
   11. PTRATIO pupil-teacher ratio by town
   12. B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
   13. LSTAT % lower status of the population
   14. MEDV Median value of owner-occupied homes in $1000's

4. Missing Attribute Values: None.

Analysis of ‘Real Estate DataSet’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arslanali4343/real-estate-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Concerns housing values in suburbs of Boston.

1. Number of Instances: 506

2. Number of Attributes: 13 continuous attributes (including "class" attribute "MEDV"), 1 binary-valued attribute.

3. Attribute Information:

   1. CRIM per capita crime rate by town
   2. ZN proportion of residential land zoned for lots over 25,000 sq.ft.
   3. INDUS proportion of non-retail business acres per town
   4. CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
   5. NOX nitric oxides concentration (parts per 10 million)
   6. RM average number of rooms per dwelling
   7. AGE proportion of owner-occupied units built prior to 1940
   8. DIS weighted distances to five Boston employment centres
   9. RAD index of accessibility to radial highways
   10. TAX full-value property-tax rate per $10,000
   11. PTRATIO pupil-teacher ratio by town
   12. B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
   13. LSTAT % lower status of the population
   14. MEDV Median value of owner-occupied homes in $1000's

4. Missing Attribute Values: None.

--- Original source retains full ownership of the source dataset ---

Context

The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.

Attribute Information

Input features in order: 1) CRIM: per capita crime rate by town 2) ZN: proportion of residential land zoned for lots over 25,000 sq.ft. 3) INDUS: proportion of non-retail business acres per town 4) CHAS: Charles River dummy variable (1 if tract bounds river; 0 otherwise) 5) NOX: nitric oxides concentration (parts per 10 million) [parts/10M] 6) RM: average number of rooms per dwelling 7) AGE: proportion of owner-occupied units built prior to 1940 8) DIS: weighted distances to five Boston employment centres 9) RAD: index of accessibility to radial highways 10) TAX: full-value property-tax rate per $10,000 [$/10k] 11) PTRATIO: pupil-teacher ratio by town 12) B: The result of the equation B=1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town 13) LSTAT: % lower status of the population

Output variable: 1) MEDV: Median value of owner-occupied homes in $1000's [k$]

Source

StatLib - Carnegie Mellon University

Relevant Papers

Harrison, David & Rubinfeld, Daniel. (1978). Hedonic housing prices and the demand for clean air. Journal of Environmental Economics and Management. 5. 81-102. 10.1016/0095-0696(78)90006-2. LINK

Belsley, David A. & Kuh, Edwin. & Welsch, Roy E. (1980). Regression diagnostics: identifying influential data and sources of collinearity. New York: Wiley LINK

Mexico's housing market demonstrates significant regional price variations, with Mexico City emerging as the most expensive area for residential property in 2024. The capital city's average house price of 3.91 million Mexican pesos far exceeds the national average of 1.73 million pesos, highlighting the stark contrast in property values across the country. This disparity reflects broader economic and demographic trends shaping Mexico's real estate landscape. Sustained growth in housing prices The Mexican housing market has experienced substantial growth over the past decade, with home prices more than doubling since 2010. By the third quarter of 2023, the nominal house price index reached 255.54 points, representing a 146 percent increase from the baseline year. Even when adjusted for inflation, the real house price index showed a notable 40 percent growth, underscoring the market's resilience and attractiveness to investors. The mortgage market is dominated by three main player types: Infonavit, Fovissste, and commercial banks including Sofomes. In 2023, Infonavit, a scheme by Mexico's National Housing Fund Institute which provides lending to workers in the formal sector, was responsible for the majority of mortgages granted to individuals. Challenges in mortgage lending Despite the overall growth in housing prices, Mexico's mortgage market has faced challenges in recent years. The number of new mortgage loans granted has declined over the past decade, falling by approximately 200,000 loans between 2008 and 2023. This decrease in lending activity may be attributed to various factors, including economic uncertainties and changing consumer preferences. The state of Mexico, which is home to 13 percent of the country's population, likely plays a significant role in shaping these trends, given its large demographic influence on the national housing market.

The average asking rent for Class A office space in Midtown Manhattan was ***** U.S. dollars per square foot in the first quarter of 2024. It was above the Manhattan average of ***** U.S. dollars, but below that of Midtown South, which was the most expensive district at ****** U.S. dollars per square foot. What is Class A real estate?Class A real estate refers to the best properties in terms of appearance, age, quality of infrastructure and location. These properties usually command the highest rental rates, due to their high quality. In the U.S., Manhattan has the most expensive rents for Class A offices.Midtown vs Midtown SouthMidtown Manhattan contains the Empire State Building, MoMA, Grand Central Station, and the United Nations Headquarters. The most expensive submarket there was Plaza District in 2024. Meanwhile, Midtown South is home to Madison Square Garden, Pennsylvania Station, Hudson Yards, and Koreatown. In 2024, the most expensive submarket there was Hudson Yards, followed by Chelsea and Hudson Square.

Analysis of ‘homeprices-multiple-variables’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/pankeshpatel/homepricesmultiplevariables on 14 February 2022.

--- Dataset description provided by original source is as follows ---

Context

Sample data of housing price. We have used this small data set to create a tutorial -- Machine learning for absolute beginners. The topic is Multivariate Regression.

Content

It has the following four attributes, describing a house - **area **: area of a house in square feet - bedrooms: number of bedrooms in a house - **age **: age of house - price: price of a house.

Area, bedrooms, and age are feature attributes and price is target attributes/variable.

Acknowledgements

Source: codebasics : https://twitter.com/codebasicshub

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Miami Housing Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/deepcontractor/miami-housing-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Content

The dataset contains information on 13,932 single-family homes sold in Miami .

Content

The dataset contains the following columns:

• PARCELNO: unique identifier for each property. About 1% appear multiple times.
• SALE_PRC: sale price ($)
• LND_SQFOOT: land area (square feet)
• TOT_LVG_AREA: floor area (square feet)
• SPEC_FEAT_VAL: value of special features (e.g., swimming pools) ($)
• RAIL_DIST: distance to the nearest rail line (an indicator of noise) (feet)
• OCEAN_DIST: distance to the ocean (feet)
• WATER_DIST: distance to the nearest body of water (feet)
• CNTR_DIST: distance to the Miami central business district (feet)
• SUBCNTR_DI: distance to the nearest subcenter (feet)
• HWY_DIST: distance to the nearest highway (an indicator of noise) (feet)
• age: age of the structure
• avno60plus: dummy variable for airplane noise exceeding an acceptable level
• structure_quality: quality of the structure
• month_sold: sale month in 2016 (1 = jan)
• LATITUDE
• LONGITUDE

--- Original source retains full ownership of the source dataset ---

The North American residential construction market, valued at $850 million in 2025, is projected to experience robust growth, driven by several key factors. A burgeoning population, particularly in urban centers, coupled with increasing household formations, fuels consistent demand for new housing units. Furthermore, low mortgage interest rates (historically, though this is subject to fluctuation) and government incentives aimed at boosting homeownership have stimulated market activity. The market segmentation reveals a strong preference for single-family homes, particularly in suburban and rural areas, alongside a notable increase in multi-family dwellings catering to urban renters and the growing demand for rental properties. New construction continues to dominate the market share, although renovation and remodeling projects represent a significant and growing segment, particularly as existing housing stock ages and requires upgrades. Leading players like Lennar Corporation, D.R. Horton, and PulteGroup are well-positioned to capitalize on these trends, utilizing innovative building techniques and sustainable materials to meet evolving consumer preferences. However, the market also faces challenges. Rising material costs, labor shortages, and increasing regulatory compliance requirements pose significant headwinds. Supply chain disruptions, though less severe than in recent years, still impact project timelines and budgets. Furthermore, fluctuations in interest rates and economic uncertainty can influence buyer confidence and affect overall market demand. Despite these hurdles, the long-term outlook for the North American residential construction market remains positive, fueled by demographic shifts and sustained investment in infrastructure development. The market is expected to maintain a compound annual growth rate (CAGR) of 4.50% from 2025 to 2033, indicating a substantial expansion in market size and value over the forecast period. The continued evolution of building technologies, focusing on energy efficiency and smart home integration, will further shape market dynamics in the coming years. Recent developments include: December 2022: In southeast Columbus, D.R. Horton intends to build homes for USD 215 million., December 2022: According to the company's fourth-quarter results call, Lennar Corp. has decided not to proceed with its plans to spin off its multifamily subsidiary, Quarterra, by the end of the year owing to adverse market circumstances., December 2022: At the southeast corner of Idlewild Street and Plantation Road in south Fort Myers, a 17-acre site is being cleared. According to Lee County documents, the area will be transformed into the 52-home neighborhood of Addison Square. The land was purchased by Pulte Homes for USD 2.4 million in a deal facilitated by Chuck Mayhugh of Mayhugh Commercial Advisors. The homes will vary in price from more than USD 500,000 and have 1,600 to 3,400 square feet of living space, with the majority of the homesites being grouped together along a sizable, central lake. According to Pulte executives, construction on the model houses should start by the spring, with some of them being done by the summer.. Notable trends are: 800,000 Housing Units Must Be Built Annually in Mexico to Keep Up with Demand.

What is the average price of residential property in the Netherlands? In the third quarter of 2024, a single-family home cost approximately 434,000 euros. There were large differences between the Dutch provinces, however. Single-family homes were most expensive in the central province of Utrecht with an average price of 731,000 euros, whereas a similar house in Groningen had an average price tag of 384,000 euros. Overall, the average price a private individual would pay when buying any type of existing residential property (such as single-family homes but also, for example, an apartment) was approximately 416,000 euros in 2023. Do the Dutch prefer to buy or to rent a house? The Netherlands had a slightly higher homeownership rate (the share of owner-occupied dwellings of all homes) in 2023 than other countries in Northwestern Europe. About 70 percent of all Dutch houses were owned, whereas this percentage was lower in Germany, France, and the United Kingdom. This is an effect of past developments: the price to rent ratio (the development of the nominal purchase price of a house divided by the annual rent of a similar place with 2015 as a base year) shows that the gap between house prices and rents has continuously widened in recent years. Despite a slight decline in the ratio due to slowing house price growth and accelerating rental growth, in 2023, the cost of buying a home had grown significantly faster relative to the cost of renting. Mortgages in the Netherlands Additionally, the Netherlands has one of the highest mortgage debts among private individuals in Europe. In 2024, total debt exceeded 839 billion euros. This has a political background, as the Dutch tax system allowed homeowners for many years to deduct interest paid on mortgage from pre-tax income for a maximum period of thirty years, essentially allowing for income support for homeowners. In the Netherlands, this system is known as hypotheekrenteaftrek. Note that since 2014, the Dutch government is slowly scaling this down, with a planned acceleration from 2020 onwards.

In December 2024, the average price of an apartment purchase in South Korea was around *** million South Korean won per square meter. The apartment price was highest in Seoul with about **** million won per square meter, almost twice as much as following the province of Gyeonggi with around *** million won per square meter.

The centrality of Seoul Boasting a population of over nine million people and accounting for a large part of the country’s GDP, the capital city is both economically attractive and notoriously expensive in terms of housing. Going by mean apartment selling prices, Seoul’s southern and southeastern districts of Gangnam and Seocho rank the highest. Changing trends For a society which traditionally expected for its younger generations to care for their elders, these norms began to soften up in the country. The first two decades of the century saw the share of one-person households more than double. As the principal age groups that reside in such households are younger people, the country is expected to continue building housing units in the future.

In 2022, the average living space per person in South Korea was about 35 square meters. This was the first increase in two years and a continuation of a slow general increase in living space per person.

Housing prices in South Korea Almost half of the country’s population lives in the metropolitan Seoul Capital Area, comprising the cities of Seoul and Incheon, as well as Gyeonggi Province. Accordingly, purchased apartment prices are especially high in and around Seoul. Within the capital, the popular Gangnam district ranks among the areas with the highest selling prices for apartments. South Korean home ownership Although all age groups favor home ownership, the rate of actual ownership increases with age. With increasing difficulty to finance housing in the city, as well as the country’s problem with a low fertility rate, the government responded by offering favorable loans to millennials. The goal is to incentivize younger generations to afford housing closer to their workplaces and with sufficient space to found families.

In 2023, the average size per dwelling in Japan was ***** square meters. When broken down by prefecture, ****** stood out as the prefecture with the largest average floor area per housing unit, while ***** had the smallest average floor area in the same year. Real estate in Japan The real estate industry is an important pillar of the Japanese economy and ranked as the third-largest industry in Japan in terms of value added in 2022. Despite this, the number of unoccupied dwellings in Japan has been steadily increasing for over thirty years. Many unoccupied homes are empty and abandoned. This trend can be attributed to two primary factors: the aging of the population and the challenges faced by empty house owners in managing and utilizing properties, as most of these homes are either managed by older individuals or family inheritance. Housing in Tokyo The monthly residential property price index in Tokyo Prefecture has experienced a significant upward trend in the past years. Tokyo holds the largest population in Japan, with approximately ** million inhabitants, making it the world’s largest urban agglomeration. The city’s appeal lies in its many renowned universities and corporate headquarters, which draws migrants from other prefectures, particularly young individuals. However, Japan is currently experiencing an investment boom in real estate, fueled by overseas investment. This has resulted in central Tokyo apartments becoming less affordable for many people.

In the first half of 2023, south-central Mumbai had the highest average real estate price rate of around 35.6 thousand Indian rupees per square foot in the Mumbai metropolitan region. Peripheral central suburbs recorded the cheapest property rates.

Mumbai’s real estate market

India’s financial capital, Mumbai’s increasing population, rapid urbanization, and employment opportunities have contributed to skyrocketing property rates in the city. Its real estate market is the most expensive in India. Since the onset of the pandemic, the demand for larger units has been on the rise. In 2023, the realty market saw a surge, especially in luxury and high-end properties catering to the affluent of the city.

New metro connectivity with suburbs

As per realtors, the inauguration of new metro lines connecting the suburbs of Mumbai will provide enhanced connectivity, ease of traveling, and reduced travel time. As a result, a rise in homebuying sentiment in the city, especially in the western suburban areas is expected. The area is considered to be one of the most preferred home-buying destinations in Mumbai with Bollywood actors, non-resident Indians, and industrialists vying for properties.