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Real Estate Price Predictor Data - PH Edition
Comodities & Real Estate
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About
Data offering details housing information for the Philippines. This dataset was scraped from the Lamudi real estate website and compiled to provide insights into property listings across the country. The original inspiration was to support the development of machine learning algorithms for price forecasting in the Philippine real estate market. The dataset includes key metrics such as location, listing price in Philippine Pesos, number of rooms, and spatial coordinates.
Columns
- Description: A general header extracted from the webpage listing. This field has 1255 unique values, with the most frequent listing being a '2BR Condo Unit for Sale in Ibiza Tower at Circulo Verde, Bagumbayan, Quezon City'.
- Location: Details the physical location of the real estate. There are 113 unique locations recorded, with Ugong, Pasig (17%) and Ortigas CBD, Pasig (10%) being the most commonly represented areas.
- Price (PHP): The price of the property in Philippine Peso. This column contains 866 unique price points, though 3% of the values are marked as 'na'.
- Bedrooms: The number of bedrooms in the property. One bedroom units are the most prevalent, accounting for 49% of the records.
- Bath: The number of bathrooms. Notably, 42% of the values in this column are 'na', followed closely by properties with 1 bathroom (41%).
- Floor_area (sqm): The described floor area in square metres.
- Land_area (sqm): The described land area in square metres. This field contains a high percentage of missing values, with 78% recorded as 'na'.
- Latitude: The geographical latitude of the listing.
- Longitude: The geographical longitude of the listing, ranging primarily between 120.55 and 125.65.
- Link: The direct website link to the original listing.
Distribution
The data is contained within a file named PH_houses_v2.csv, totalling 378.64 kB. It is structured across 10 distinct columns and features 1500 total records. All records are valid across the dataset. While the data integrity is sound, several fields exhibit a significant number of ‘na’ values, particularly ‘Land_area (sqm)’ (78% 'na') and ‘Bath’ (42% 'na').
Usage
This dataset is suitable for training machine learning models aimed at real estate price prediction. It can be used for detailed geospatial analysis, allowing researchers to map price variations against latitude and longitude (a Tableau visualization for this purpose has already been created). Additionally, it supports general market research to identify dominant property types and locations within the Philippines.
Coverage
The scope is geographically limited to the Philippine real estate market, with a focus on listings obtained from lamudi.com.ph. The data set represents a single snapshot collected via web scraping. The expected update frequency is Never.
License
CC0: Public Domain
Who Can Use It
Data Scientists and Machine Learning Engineers who require high-quality, tagged regional data for developing real estate price forecasting models. Market Researchers and Real Estate Analysts seeking primary data on listing prices, property features, and spatial distribution in urban centres like Ugong, Pasig, and Ortigas CBD, Pasig.
Dataset Name Suggestions
- Philippine Property Listings from Lamudi
- PH Housing Data for ML
- Real Estate Price Predictor Data - PH Edition
- Scraped Philippine Housing Listings
Attributes
Original Data Source: Real Estate Price Predictor Data - PH Edition
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