Key information about House Prices Growth

• Taiwan house prices grew 0.1% YoY in Sep 2025, following a decrease of 0.1% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 2002 to Sep 2025, with an average growth rate of 15.1%.
• House price data reached an all-time high of 20.9% in Mar 2010 and a record low of -6.0% in Mar 2016.

Housing Index in Taiwan decreased to 164.39 points in the second quarter of 2025 from 168.42 points in the first quarter of 2025. This dataset provides - Taiwan House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

In 2023, the Taiwan Real Estate Market reached a value of USD 199.1 million, and it is projected to surge to USD 317.6 million by 2030.

In Taiwan Prefabricated Housing Market is projected to grow from USD 21.5 billion in 2025 to USD 38.6 billion by 2031, at a CAGR of 10.1%

In accordance with the Ministry of the Interior's policy, conduct dynamic analysis of the Taipei City real estate market every quarter.

USD 6.62 Billion in 2024; projected USD 11.64 Billion by 2033; CAGR 6.49%.

Context

This is a dataset from the Taiwan Open Data Platform. To make the dataset widely used and more valuable to show its hidden information, several data preprocessing jobs were done including different ways of handling missing values, translation, and extraction of more features.

• district: 12 administrative districts in Taipei City.
• transaction_type: land, building or carpark
• land_shift_area: shifted land area in a transaction (square meter)
• urban_land_use: business, accommodation or others
• main_use: similar to urban_land_use
• main_building_material: concrete, wood, stone...
• complete_year: year when the building is completed (should plus 1,911 to align the standard of Gregorian Calendar)
• building_shift_total_area: shifted building area (square meter)
• num_room
• num_hall
• num_toilet
• num_partition: if there is partition or not
• management_org: if there is a management organization for a building
• total_ntd: total price in NT dollar (1 USD is around 30 NTD)
• unit_ntd: price per square meter
• carpark_category
• carpark_shift_area: (square meter)
• carpark_ntd
• transaction_year
• transaction_month
• building_age
• number_of_land: number of land in a transaction
• number_of_building: number of building in a transaction
• number_of_carpark: number of carpark in a transaction

Original Data Source: https://plvr.land.moi.gov.tw/DownloadOpenData (Q1, 2020)

Inspiration

Who can predict the housing price of Taipei City? What insight do we get in the housing market?

In September 2022, the urban land price index in New Taipei City had a value of ******. From 2013 to early 2016, the land prices increased rapidly from an index value of around ** to over ***. Since then the price had stabilized at an index value slightly below 100. New Taipei City was the most populous city in Taiwan and encircled Taipei City.

Real estate market urban land price changes, the concentration of domestic bank loans in residential real estate and commercial real estate loans.

Taiwan Real Estate Software Market is expected to grow during 2025-2031

USD 329.32 Million in 2024; projected USD 670.32 Million by 2033; CAGR 8.22%.

Taiwan Asset: HH: NF: Real Estate (Land evaluated at current market price) data was reported at 52,532,100.000 NTD mn in 2023. This records an increase from the previous number of 51,385,900.000 NTD mn for 2022. Taiwan Asset: HH: NF: Real Estate (Land evaluated at current market price) data is updated yearly, averaging 47,128,893.000 NTD mn from Dec 2009 (Median) to 2023, with 15 observations. The data reached an all-time high of 52,532,100.000 NTD mn in 2023 and a record low of 28,534,577.000 NTD mn in 2009. Taiwan Asset: HH: NF: Real Estate (Land evaluated at current market price) data remains active status in CEIC and is reported by Directorate-General of Budget, Accounting and Statistics, Executive Yuan. The data is categorized under Global Database’s Taiwan – Table TW.AB014: Balance Sheet: Households.

1. Real estate transaction information includes location, area, total price, etc. 2. This dataset is updated every 10 days.

USD 132.51 Million in 2024; projected USD 262.49 Million by 2033; CAGR 7.92%.

The Taiwan Facility Management Market Report is Segmented by Service Type (Hard Services, Soft Services), Offering Type (In-House, Outsourced), End-User Industry (Commercial, Hospitality, Institutional and Public Infrastructure, Healthcare, Industrial and Process, and More), and Geography (Taiwan). The Market Forecasts are Provided in Terms of Value (USD).

Monthly house price indices of 17 counties/municipalities in Taiwan.

In 2022, the index value of urban land prices in Taipei city was ******. Just like in Taiwan's other special municipalities, from 2013 to 2015, land prices increased rapidly before they settled around the index value of 100. However, the price spike in Taipei City was much higher than in the neighboring municipality of New Taipei City.

Unit roots and stationary properties of county/city-level house price indices of Taiwan.

Real Estate Dataset Description

This dataset contains information on real estate transactions. It includes various attributes related to each property and its transaction details. The dataset comprises 414 entries, each representing a unique transaction. Below is a detailed description of the dataset's columns:

Trans date:

Type: float64 Description: The date of the transaction in fractional year format. For example, 2012.917 represents a transaction that occurred in late 2012. House age:

Type: float64 Description: The age of the house in years at the time of the transaction. This value indicates how old the house is. Distance station:

Type: float64

Description: The distance from the house to the nearest station in meters. This value reflects the accessibility of public transportation. No of stores:

Type: int64

Description: The number of convenience stores located within a certain radius of the house. This value indicates the availability of nearby amenities. Latitude:

Type: float64

Description: The geographical latitude of the house location. This value is part of the coordinates indicating the house's location. Longitude:

Type: float64

Description: The geographical longitude of the house location. This value is part of the coordinates indicating the house's location. House Price:

Type: float64

Description: The price of the house in local currency. This value represents the transaction price at which the house was sold.

Column Description:

Number of Entries: 414

Number of Columns: 7

Columns and Data Types:

Trans date (float64): The transaction date.

House age (float64): The age of the house.

Distance station (float64): The distance to the nearest station.

No of stores (int64): The number of convenience stores nearby.

Latitude (float64): The latitude of the house location.

Longitude (float64): The longitude of the house location.

House Price (float64): The price of the house.

Acknowledgement:

The data originates from Sindian Dist., New Taipei City, Taiwan, and is used for regression analysis to predict real estate prices based on these features. This dataset is available on the UCI Machine Learning Repository:

https://archive.ics.uci.edu/dataset/477/real+estate+valuation+data+set

Conclusion: Real estate datasets are valuable resources for understanding market trends, making informed decisions, and conducting research in the real estate industry. By leveraging these datasets, stakeholders can gain insights into property markets, optimize investment strategies, and contribute to the sustainable development of real estate markets.

Context

To learn more on SVM Algorithm and specifically on Regression, I have downloaded the data set of Real Estate Valuation data set from UCI Machine Learning Repository.

Content

This is a Dataset downloaded from UCI Machine Learning Repository. Description as per UCI site : The market historical data set of real estate valuation are collected from Sindian Dist., New Taipei City, Taiwan.

Attribute Information

The inputs are as follows X1=the transaction date (for example, 2013.250=2013 March, 2013.500=2013 June, etc.) X2=the house age (unit: year) X3=the distance to the nearest MRT station (unit: meter) X4=the number of convenience stores in the living circle on foot (integer) X5=the geographic coordinate, latitude. (unit: degree) X6=the geographic coordinate, longitude. (unit: degree)

The output is as follow Y= house price of unit area (10000 New Taiwan Dollar/Ping, where Ping is a local unit, 1 Ping = 3.3 meter squared)