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.

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.

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.

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

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.

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

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

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?

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

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.

In 2024, mainland China invested around *** million U.S. dollars in Taiwan's real estate sector. In comparison, the investments exceeded *** million U.S. dollars in 2014.

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.

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.

This statistic shows the number of insured workers in the real estate industry in Taiwan as of December 2021, by area. In Taoyuan city of Taiwan, ****** people in the real estate industry were enrolled in the labor insurance program.

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

Taiwan AFI: Real Estate data was reported at 49.887 USD mn in Mar 2025. This records an increase from the previous number of 0.000 USD mn for Feb 2025. Taiwan AFI: Real Estate data is updated monthly, averaging 18.107 USD mn from Jan 2006 (Median) to Mar 2025, with 231 observations. The data reached an all-time high of 287.974 USD mn in Jul 2017 and a record low of 0.000 USD mn in Feb 2025. Taiwan AFI: Real Estate data remains active status in CEIC and is reported by Investment Commission, Ministry of Economic Affairs. The data is categorized under Global Database’s Taiwan – Table TW.O002: Foreign Investment Approved: Investment Commission: By Industry.

Snapshot img

Physical Intellectual Property Market Size 2025-2029

The physical intellectual property market size is valued to increase USD 3.41 billion, at a CAGR of 7.4% from 2024 to 2029. Growing complexity of ICs will drive the physical intellectual property market.

Major Market Trends & Insights

North America dominated the market and accounted for a 51% growth during the forecast period.
By Application - Mobile computing devices segment was valued at USD 2.86 billion in 2023
By End-user - Semiconductor segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 72.35 million
Market Future Opportunities: USD 3411.70 million
CAGR : 7.4%
North America: Largest market in 2023

Market Summary

The market encompasses the licensing, buying, and selling of tangible inventions and creations, primarily focusing on core technologies and applications such as semiconductors, biotechnology, and mechanical designs. With the growing complexity of integrated circuits and the proliferation of wireless technologies, the demand for configurable semiconductor IP continues to surge. Service types or product categories, including patent licensing, patent enforcement, and patent valuation, play a crucial role in this market. Regulatory compliance, particularly in the context of intellectual property laws and international trade agreements, poses challenges for market participants. Looking forward, the market is expected to unfold with significant opportunities, particularly in emerging economies, as they increasingly prioritize innovation and IP protection.
According to recent reports, the patent licensing segment is projected to account for over 60% of the market share, underscoring its dominance in the landscape.

What will be the Size of the Physical Intellectual Property Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Physical Intellectual Property Market Segmented and what are the key trends of market segmentation?

The physical intellectual property industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Mobile computing devices
  Consumer electronic devices
  Automotive
  Industrial automation
  Others


End-user

  Semiconductor
  Manufacturing
  IT and telecom
  Others


Type

  Patents
  Licensing
  Copyrights
  Architectural design rights
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    Japan
    South Korea
    Taiwan


  Rest of World (ROW)

By Application Insights

The mobile computing devices segment is estimated to witness significant growth during the forecast period.

The market encompasses various aspects, including intellectual property licensing, brand asset valuation, copyright infringement litigation, technology transfer agreements, trademark registration process, ip portfolio optimization, design patent applications, trade secret protection, competitive intelligence gathering, ip asset monetization, knowledge management systems, utility patent prosecution, ip litigation strategies, patent portfolio management, patent landscape analysis, ip enforcement actions, ip valuation methodologies, licensing revenue forecasting, franchise agreements, portfolio diversification strategy, transactional ip law, technology valuation models, ip asset registry, IP risk assessment, technology commercialization, confidentiality agreements, royalty income streams, software license compliance, non-compete clauses, digital rights management, data privacy regulations, and open-source software licensing. In the mobile computing devices segment, the demand for physical intellectual property is on the rise due to the increasing need for higher processing power in mobile and other computing devices.

This trend is fueled by the growing popularity of mobile computing devices such as smartphones, tablets, laptops, and ultra-books. Chinese manufacturers like BBK Electronics, Huawei Technologies, and Xiaomi are leading this segment with their competitively priced devices offering upgraded technologies. The disposable income of consumers in developing countries, particularly India, is another significant factor contributing to the growth of mobile computing devices. Additionally, the increasing internet penetration is playing a crucial role in driving the demand for these devices. According to recent studies, the adoption of mobile computing devices has grown by 18.7%, and it is projected to expand by 25.6% in the coming years.

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The Mobile computing devices segment was valued at USD 2.86 billion in 2019 and showed a gr

Comprehensive Airbnb dataset for Tainan, Taiwan providing detailed vacation rental analytics including property listings, pricing trends, host information, review sentiment analysis, and occupancy rates for short-term rental market intelligence and investment research.

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

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)