Housing Index in South Korea increased to 94 points in October from 93 points in September of 2025. This dataset provides - South Korea House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Residential Property Prices in South Korea increased 0.11 percent in June of 2025 over the same month in the previous year. This dataset includes a chart with historical data for South Korea Residential Property Prices.

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 October 2025, the mean purchase price of housing in Seoul, South Korea, amounted to around *** million South Korean won. The average price of apartments amounted to around **** billion won, while the price of detached houses was about **** billion South Korean won. Apartments in South Korea Among all housing types, apartments are the most expensive, costing more than *** 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 ** 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 **** 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.

Introduction

This Korean Call Center Speech Dataset for the Real Estate industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Korean -speaking Real Estate customers. With over 30 hours of unscripted, real-world audio, this dataset captures authentic conversations between customers and real estate agents ideal for building robust ASR models.

Curated by FutureBeeAI, this dataset equips voice AI developers, real estate tech platforms, and NLP researchers with the data needed to create high-accuracy, production-ready models for property-focused use cases.

Speech Data

The dataset features 30 hours of dual-channel call center recordings between native Korean speakers. Captured in realistic real estate consultation and support contexts, these conversations span a wide array of property-related topics from inquiries to investment advice offering deep domain coverage for AI model development.

Topic Diversity

This speech corpus includes both inbound and outbound calls, featuring positive, neutral, and negative outcomes across a wide range of real estate scenarios.

Such domain-rich variety ensures model generalization across common real estate support conversations.

Transcription

All recordings are accompanied by precise, manually verified transcriptions in JSON format.

These transcriptions streamline ASR and NLP development for Korean real estate voice applications.

Metadata

Detailed metadata accompanies each participant and conversation:

This enables smart filtering, dialect-focused model training, and structured dataset exploration.

Usage and Applications

This dataset is ideal for voice AI and NLP systems built for the real estate sector:

Korea Median Housing Price: Total: 6 Large Cities: Incheon data was reported at 21,320.232 KRW tt in Sep 2018. This records an increase from the previous number of 21,262.911 KRW tt for Aug 2018. Korea Median Housing Price: Total: 6 Large Cities: Incheon data is updated monthly, averaging 19,100.706 KRW tt from Apr 2013 (Median) to Sep 2018, with 66 observations. The data reached an all-time high of 21,331.862 KRW tt in Jan 2018 and a record low of 17,046.938 KRW tt in Sep 2013. Korea Median Housing Price: Total: 6 Large Cities: Incheon 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.

House Price Index MoM in South Korea increased to 0.30 percent in October from 0.10 percent in September of 2025. This dataset includes a chart with historical data for South Korea House Price Index MoM.

Dataset

This dataset was created by Meongsu Zack Lee

Contents

This is a dataset based on real estate transaction data provided by Korean public API.

Currently, only some regions and periods are available, but will continue to be provided through updates.

In addition, we will provide more information related to real estate through more updates.

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.

Comprehensive dataset containing 603 verified Row house businesses in South Korea with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 25 verified House locations in South Korea with complete contact information, ratings, reviews, and location data.

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.

Housing Price Index: Row Houses data was reported at 115.224 Sep2003=100 in Dec 2007. This records an increase from the previous number of 114.602 Sep2003=100 for Nov 2007. Housing Price Index: Row Houses data is updated monthly, averaging 91.028 Sep2003=100 from Jan 1986 (Median) to Dec 2007, with 264 observations. The data reached an all-time high of 115.224 Sep2003=100 in Dec 2007 and a record low of 56.359 Sep2003=100 in Jun 1987. Housing Price Index: Row Houses data remains active status in CEIC and is reported by Kookmin Bank. The data is categorized under Global Database’s Korea – Table KR.EB006: Housing Price Index: Kookmin Bank: Sep 2003=100.

Price-To-Book-Ratio Time Series for HDC Holdings Co Ltd. HDC HOLDINGS CO.,Ltd engages in real estate development and construction activities in South Korea. Its portfolio includes high-rise buildings, roads, bridges, ports, plants, small and medium-sized houses, commercial districts and parking lot sites, hotels, and shopping malls, as well as operates a duty-free shop. The company also offers facility management services, such as building management, cleaning, parking/security, and general affairs/clerical support; real estate asset management service comprising operation, leasing, building management, and management consulting services. In addition, it engages in interior, remodeling, and landscaping services; and operates special care facility for the elderly people. Further, the company is involved in the petrochemical business; production of precast concrete; manufacture of pianos; and operation of the professional football club. HDC HOLDINGS CO.,Ltd was founded in 1976 and is based in Seoul, South Korea.

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.

Comprehensive dataset containing 36 verified Country house businesses in South Korea with complete contact information, ratings, reviews, and location data.

Forecast: Real Estate Output in South Korea 2022 - 2026 Discover more data with ReportLinker!

This dataset contains key characteristics about the data described in the Data Descriptor The ENERTALK dataset, 15 Hz electricity consumption data from 22 houses in Korea. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format 
    Versioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.

Forecast: Production of Real Estate in South Korea 2024 - 2028 Discover more data with ReportLinker!