Residential Property Price Index in Nigeria: A Data Mining Approach

Michael Kalu Mba; Olutope Olufunso Olorunfemi; Adeyemi Adebayo Adeboye; Valli Asabe Takaya; Mohammed Gana Mohammed; Amechi Henry Igweze; Lailah Sanusi Gumbi; Ogochukwu Gina Onumonu

doi:doi:10.11648/j.ijber.20231201.14

Residential Property Price Index in Nigeria: A Data Mining Approach

Michael Kalu Mba, Olutope Olufunso Olorunfemi, Adeyemi Adebayo Adeboye, Valli Asabe Takaya, Mohammed Gana Mohammed, Amechi Henry Igweze, Lailah Sanusi Gumbi, Ogochukwu Gina Onumonu

Published in International Journal of Business and Economics Research (Volume 12, Issue 1)

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), 2024. Published by Science Publishing Group

Keywords

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

References

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[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.
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[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.
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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

Michael Kalu Mba

Department of Statistics, Central Bank of Nigeria, Abuja, Nigeria
Olutope Olufunso Olorunfemi

Department of Statistics, Central Bank of Nigeria, Abuja, Nigeria
Adeyemi Adebayo Adeboye

Department of Statistics, Central Bank of Nigeria, Abuja, Nigeria
Valli Asabe Takaya

Department of Statistics, Central Bank of Nigeria, Abuja, Nigeria
Mohammed Gana Mohammed

Department of Statistics, Central Bank of Nigeria, Abuja, Nigeria
Amechi Henry Igweze

Department of Statistics, Central Bank of Nigeria, Abuja, Nigeria
Lailah Sanusi Gumbi

Department of Statistics, Central Bank of Nigeria, Abuja, Nigeria
Ogochukwu Gina Onumonu

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

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Plain Text BibTeX RIS

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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download