Housing Index in South Korea decreased to 93 points in February from 93.10 points in January of 2025. This dataset provides - South Korea House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset was generated for analyzing the economic impacts of subway networks on housing prices in metropolitan areas. The provision of transit networks and accompanying improvement in accessibility induce various impacts and we focused on the economic impacts realized through housing prices. As a proxy of housing price, we consider the price of condominiums, the dominant housing type in South Korea. Although our focus is transit accessibility and housing prices, the presented dataset is applicable to other studies. In particular, it provides a wide range of variables closely related to housing price, including housing properties, local amenities, local demographic characteristics, and control variables for the seasonality. Many of these variables were scientifically generated by our research team. Various distance variables were constructed in a geographic information system environment based on public data and they are useful not only for exploring environmental impacts on housing prices, but also for other statistical analyses in regard to real estate and social science research. The four metropolitan areas covered by the data—Busan, Daegu, Daejeon, and Gwangju—are independent of the transit systems of Greater Seoul, providing accurate information on the metropolitan structure separate from the capital city.

As of December 2024, the mean purchase price of housing in Seoul, South Korea, amounted to around 866 million South Korean won. The average price of detached houses amounted to around 1.14 billion won while the price of apartments was about 1.13 billion South Korean won. Apartments in South Korea Among all housing types, apartments are the most expensive, costing more than one billion South Korean won on average. Living in apartments is typical for Seoul, as an increasing number of citizens move towards the city, causing high population density. As of 2022, more than 40 percent of all households were living in apartments, excluding alternative housing, such as officetels or goshiwons. Gangnam Style Based on the average selling price of apartments in Seoul, Gangnam is the most expensive area in Seoul to live in, with an average sales price of around 2.38 billion South Korean won. The area became internationally known due to the viral YouTube hit Gangnam Style by South Korean artist PSY. Since Gangnam is known for its wealthy citizens, the song was inspired by their mannerisms.

House Price Index MoM in South Korea increased to 0 percent in February from -0.10 percent in January of 2025. This dataset includes a chart with historical data for South Korea House Price Index MoM.

Residential Property Prices in South Korea decreased 0.36 percent in September of 2024 over the same month in the previous year. This dataset includes a chart with historical data for South Korea Residential Property Prices.

Korea Median Housing Price: Total: 6 Large Cities: Daegu data was reported at 23,192.626 KRW tt in Jun 2018. This records an increase from the previous number of 23,160.785 KRW tt for May 2018. Korea Median Housing Price: Total: 6 Large Cities: Daegu data is updated monthly, averaging 22,210.310 KRW tt from Apr 2013 (Median) to Jun 2018, with 63 observations. The data reached an all-time high of 23,518.126 KRW tt in Jan 2016 and a record low of 15,455.327 KRW tt in Apr 2013. Korea Median Housing Price: Total: 6 Large Cities: Daegu data remains active status in CEIC and is reported by Kookmin Bank. The data is categorized under Global Database’s Korea – Table KR.EB033: Median Housing Price: Kookmin Bank.

Home Ownership Rate in South Korea increased to 56.40 percent in 2023 from 56.20 percent in 2022. This dataset provides the latest reported value for - South Korea Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Hedonic Price Model, used in existing house price modeling, may not address the relationship between house prices and streetscapes perceived at the human eye level. Therefore, in this study, we analyzed the relationship between streetscapes perceived at eye level and single-family home prices in Seoul, Korea, using computer vision technology and machine learning algorithms. We used transaction data for 13,776 single-family housing sales between 2017 and 2019. To measure visually perceived streetscapes, this study used the Deeplab V3+ deep-learning model with 233,106 Google Street View panoramic images. Then, the best machine-learning model was selected by comparing the explanatory powers of the hedonic price model and all alternative machine-learning models. According to the results, the Gradient Boost model, a representative ensemble machine learning model, performed better than XGBoost, Random Forest, and Linear Regression models in predicting single-family house prices. In addition, this study used an interpretable machine learning model of the SHAP method to identify key features that affect single-family home price prediction. This solves the "black box" problem of machine learning models. Finally, by analyzing the nonlinear relationship and interaction effects between perceived streetscape characteristics and house prices, we easily and quickly identified the relationship between variables the hedonic price model partially considers.

Introduction

Welcome to the Korean Scripted Monologue Speech Dataset for the Real Estate Domain. This meticulously curated dataset is designed to advance the development of Korean language speech recognition models, particularly for the Real Estate industry.

Speech Data

This training dataset comprises over 6,000 high-quality scripted prompt recordings in Korean. These recordings cover various topics and scenarios relevant to the Real Estate domain, designed to build robust and accurate customer service speech technology.

Each scripted prompt is crafted to reflect real-life scenarios encountered in the Real Estate domain, ensuring applicability in training robust natural language processing and speech recognition models.

Transcription Data

In addition to high-quality audio recordings, the dataset includes meticulously prepared text files with verbatim transcriptions of each audio file. These transcriptions are essential for training accurate and robust speech recognition models.

Price to Rent Ratio in South Korea increased to 102.15 in the fourth quarter of 2024 from 102.02 in the third quarter of 2024. This dataset includes a chart with historical data for South Korea Price to Rent Ratio.

South Korea Price to Income Ratio: sa data was reported at 84.915 2015=100 in 2023. This records a decrease from the previous number of 93.096 2015=100 for 2022. South Korea Price to Income Ratio: sa data is updated yearly, averaging 118.826 2015=100 from Dec 1986 (Median) to 2023, with 38 observations. The data reached an all-time high of 409.198 2015=100 in 1986 and a record low of 84.915 2015=100 in 2023. South Korea Price to Income Ratio: sa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s South Korea – Table KR.OECD.AHPI: House Price Index: Seasonally Adjusted: OECD Member: Annual. Nominal house prices divided by nominal disposable income per head. Net household disposable income is used. The population data come from the OECD national accounts database.

South Korea Standardised Price-Income Ratio: sa data was reported at 53.118 Ratio in 2024. This records a decrease from the previous number of 53.847 Ratio for 2023. South Korea Standardised Price-Income Ratio: sa data is updated yearly, averaging 74.953 Ratio from Dec 1986 (Median) to 2024, with 39 observations. The data reached an all-time high of 259.484 Ratio in 1986 and a record low of 53.118 Ratio in 2024. South Korea Standardised Price-Income Ratio: sa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s South Korea – Table KR.OECD.AHPI: House Price Index: Seasonally Adjusted: OECD Member: Annual. Nominal house prices divided by nominal disposable income per head. Net household disposable income is used. The population data come from the OECD national accounts database. The long-term average is calculated over the whole period available when the indicator begins after 1980 or after 1980 if the indicator is longer. This value is used as a reference value. The ratio is calculated by dividing the indicator source on this long-term average, and indexed to a reference value equal to 100.

South Korea House Price Index: Real: sa data was reported at 92.295 2015=100 in 2024. This records a decrease from the previous number of 94.169 2015=100 for 2023. South Korea House Price Index: Real: sa data is updated yearly, averaging 100.000 2015=100 from Dec 1986 (Median) to 2024, with 39 observations. The data reached an all-time high of 160.193 2015=100 in 1990 and a record low of 76.676 2015=100 in 2001. South Korea House Price Index: Real: sa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s South Korea – Table KR.OECD.AHPI: House Price Index: Seasonally Adjusted: OECD Member: Annual. Nominal house prices deflated using the private consumption deflator from the national account statistics. More information on the nominal house prices are available on the nominal house price indicators selection.