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Residential Property Price Index in Nigeria: A Data Mining Approach

Received: 9 November 2022     Accepted: 13 December 2022     Published: 6 February 2023
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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.

Published in International Journal of Business and Economics Research (Volume 12, Issue 1)
DOI 10.11648/j.ijber.20231201.14
Page(s) 27-33
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

RPPI, Prices, Residential Property, Web Scraping, House Prices

References
[1] Gayen, I and Maiti, S. S (2020). Compilation of House Price Index using Big Data Techniques, RBI Bulletin July 2020.
[2] EUROSTAT (2013). Handbook on Residential Property Prices Indices (rppis) EUROSTAT Methodologies and Working Papers.
[3] Eurostat. (2020). Practical Guidelines on Web Scraping for the HICP. EUROPEAN COMMISSION EUROSTAT. Directorate C: Macro-Economic Statistics. Unit C-4: Price Statistics. Purchasing Power Parities. Housing Statistics. European Commission. Available online: https://ec.europa.eu/eurostat/documents/272892/12032198/Guidelines-web-scraping-HICP-11-2020.pdf/ (accessed on 1 December 2021).
[4] Eurostat. (2021). Internet Purchases by Individuals [Data Base]. Available online: https://ec.europa.eu/eurostat/web/digital-economyand-society/data/database (accessed on 1 December 2021).
[5] IMF (2020). Residential Property Index, practicalcompilation Guide. International Monetary Fund.
[6] Benedetti, I., Tiziana L., Palumbo, Land Brandon M. R (2022). Computation of High-Frequency Sub-National Spatial Consumer Price Indexes Using Web Scraping Techniques. Economies 10:95.
[7] Mehrhoff, Jens. (2019). Introduction–The Value Chain of Scanner and Web Scraped Data. Economieet Statistique 509: 5-11.
[8] de Haan, J., R, and Scholz. M. (2021). Price Measurement Using Scanner Data: Time-Product Dummy Versus Time Dummy Hedonic Indexes. Review of Income and Wealth 67: 394–417.
[9] UNCTAD. (2020). COVID-19 and E-Commerce, Finding from a Survey of Online Consumers in 9 Countries; Geneva: United Nation Conference on trade and Development, UNCTAD.
[10] Available online: https://unctad.org/system/files/official-document/dtlstictinf2020d1_en.pdf (accessed on 1 December 2021).
[11] Lyons, R. C. (2019). Can list prices accurately capture housing price trends? Insights from extreme markets conditions. Finance Research Letters, 30, 228-232.
[12] Harchaoui, T. M., and Janssen. R. V (2018). How can big data enhance the timeliness of official statistics? The case of the US consumer price index. International Journal of Forecasting 34: 225–34.
[13] OECD. (2020). E-Commerce in the Time of COVID-19, Tackling Coronavirus (COVID-19) Contributing to a Global Effort. Paris, October.
[14] Konny, C, Williams, B and Friedman, D. (2019). Big Data in the US Consumer Price Index: Experiences and Plans. In Big Data for 21st Century Economic Statistics. Chicago: University of Chicago Press.
[15] Dumbacher, B, and Capps, C. (2016). Big data methods for scraping government tax revenue from the web. Paper presented at the Joint Statistical Meetings, Section on Statistical Learning and Data Science, Chicago, IL, USA, July 30-June 4.
[16] Bricongne, J, Meunier, B and Sylvain, P (2021). Web Scraping Housing Prices in Real-time: The COVID-19 Crisis in the UK. Working Paper, Banque de France.
[17] Souza, T. G, Fernanda. D. R. F, Fernandes, V. D and Pedrassoli, J. C (2021). Exploratory Spatial Analysis of Housing Prices Obtained from Web Scraping Technique. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 43: 135–40.
[18] Statistics Canada (2019). Web Scraping. Available online: https://www.statcan.gc.ca/eng/our-data/where/web-scraping (accessed on1 December 2021).
[19] Juszczak, A. (2021). The use of web-scraped data to analyze the dynamics of footwear prices. Journal of Economics and Management 43: 251–69.
Cite This Article
  • APA Style

    Michael Kalu Mba, Olutope Olufunso Olorunfemi, Adeyemi Adebayo Adeboye, Valli Asabe Takaya, Mohammed Gana Mohammed, et al. (2023). Residential Property Price Index in Nigeria: A Data Mining Approach. International Journal of Business and Economics Research, 12(1), 27-33. https://doi.org/10.11648/j.ijber.20231201.14

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    ACS Style

    Michael Kalu Mba; Olutope Olufunso Olorunfemi; Adeyemi Adebayo Adeboye; Valli Asabe Takaya; Mohammed Gana Mohammed, et al. Residential Property Price Index in Nigeria: A Data Mining Approach. Int. J. Bus. Econ. Res. 2023, 12(1), 27-33. doi: 10.11648/j.ijber.20231201.14

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    AMA Style

    Michael Kalu Mba, Olutope Olufunso Olorunfemi, Adeyemi Adebayo Adeboye, Valli Asabe Takaya, Mohammed Gana Mohammed, et al. Residential Property Price Index in Nigeria: A Data Mining Approach. Int J Bus Econ Res. 2023;12(1):27-33. doi: 10.11648/j.ijber.20231201.14

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  • @article{10.11648/j.ijber.20231201.14,
      author = {Michael Kalu Mba and Olutope Olufunso Olorunfemi and Adeyemi Adebayo Adeboye and Valli Asabe Takaya and Mohammed Gana Mohammed and Amechi Henry Igweze and Lailah Sanusi Gumbi and Ogochukwu Gina Onumonu},
      title = {Residential Property Price Index in Nigeria: A Data Mining Approach},
      journal = {International Journal of Business and Economics Research},
      volume = {12},
      number = {1},
      pages = {27-33},
      doi = {10.11648/j.ijber.20231201.14},
      url = {https://doi.org/10.11648/j.ijber.20231201.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijber.20231201.14},
      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.},
     year = {2023}
    }
    

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  • TY  - JOUR
    T1  - Residential Property Price Index in Nigeria: A Data Mining Approach
    AU  - Michael Kalu Mba
    AU  - Olutope Olufunso Olorunfemi
    AU  - Adeyemi Adebayo Adeboye
    AU  - Valli Asabe Takaya
    AU  - Mohammed Gana Mohammed
    AU  - Amechi Henry Igweze
    AU  - Lailah Sanusi Gumbi
    AU  - Ogochukwu Gina Onumonu
    Y1  - 2023/02/06
    PY  - 2023
    N1  - https://doi.org/10.11648/j.ijber.20231201.14
    DO  - 10.11648/j.ijber.20231201.14
    T2  - International Journal of Business and Economics Research
    JF  - International Journal of Business and Economics Research
    JO  - International Journal of Business and Economics Research
    SP  - 27
    EP  - 33
    PB  - Science Publishing Group
    SN  - 2328-756X
    UR  - https://doi.org/10.11648/j.ijber.20231201.14
    AB  - 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.
    VL  - 12
    IS  - 1
    ER  - 

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Author Information
  • Department of Statistics, Central Bank of Nigeria, Abuja, Nigeria

  • Department of Statistics, Central Bank of Nigeria, Abuja, Nigeria

  • Department of Statistics, Central Bank of Nigeria, Abuja, Nigeria

  • Department of Statistics, Central Bank of Nigeria, Abuja, Nigeria

  • Department of Statistics, Central Bank of Nigeria, Abuja, Nigeria

  • Department of Statistics, Central Bank of Nigeria, Abuja, Nigeria

  • Department of Statistics, Central Bank of Nigeria, Abuja, Nigeria

  • Department of Statistics, Central Bank of Nigeria, Abuja, Nigeria

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