Residential Property Price Index in Nigeria: A Data Mining Approach

Abstract

We employ the web-scraping technique and IMF residential property prices index methodology outlined in the latest RPPI practical compilation guide to compute the Nigeria’s Real Estate property Price Index (RPPI). The data was scraped from one of the largest real estate website in Nigeria hosting the largest real estate ads online. A total of 35,957 residential property sales ads comprising of 30,693 house and 5,264 flat/apartment listing from October 2021 to October 2022 was used for the study. A web scraping code was implemented in R-statistics to get the data. The asking price and other related information gotten from the website was used to compute the overall RPPI and its sub indices (for house and flats/apartments). The findings present the RPP national (total) index and sub-indices for the residential building (house) and residential flat/apartment. While the various data sources used in generating data for the RPPI computation have their advantages and disadvantages, the web scraping method provides a very timely approach, as data can be scraped almost immediately. This ensures timely policy decisions and implementation and also reduce the cost of survey tremendously if not totally. The study recommends the use of the web scraping technique in the generation of RPPI data to ensure timely policy decisions and internationally acceptable standard of RPPI compilation. With the web scraping approach to data collection, high frequency RPPI like monthly or weekly may be computed for the country.