Analysis of Spatial Factors Affecting Rental House Prices: A Case Study of Nyeri Town Constituency, Kenya

Evanson Ndung’u Kimani^1, , Bartholomew Thiong’o Kuria¹ and Moses Murimi Ngigi¹

¹Institute of Geomatics, GIS and Remote Sensing, Dedan Kimathi University of Technology, Private Bag-10143, Dedan Kimathi, Nyeri, Kenya

Cite this paper

Evanson Ndung’u Kimani, Bartholomew Thiong’o Kuria and Moses Murimi Ngigi. Analysis of Spatial Factors Affecting Rental House Prices: A Case Study of Nyeri Town Constituency, Kenya. Journal of Geosciences and Geomatics. 2021; 9(3):110-123. doi: 10.12691/JGG-9-3-2

Abstract

Rental properties transactions have steadily increased annually by 2.7% globally since 2017. For a developing country like Kenya the transactions contributes 8.1% of Kenya’s gross domestic product according to 2019 national accounts statistics as it forms the basis of basic human need, shelter. It is projected to increase to 22% by 2020 in line with Government’s ‘big four’ agenda among them affordable housing, thus attracting investors constructing both commercial and residential houses. Investors in the real estate sector use a range of methods to determine rental house prices. Diverse factors have been noted to influence rental house prices some having higher effect than others. This research endeavors to analyze the spatial factors that affect rental houses prices in Nyeri town constituency, Kenya and determine their relationship to their current respective prices. Primary data was collected from rental house owners and assigned managing estate agents via structured questionnaires distributed through purposive sampling method. Guided by literature and data collected, roads, land value, houses and population data alongside secondary data was acquired and analyzed using the spatial hedonic model. Through GIS technologies, spatial factors identified from the data collected affecting rental house prices are analyzed and their relationship with rental house prices was determined. Regression and multicriteria analysis assigned different weights to the various factors as they were noted to influence the prices differently. From analytic hierarchy process, varying percentages were deduced on all the factors. Study results indicates different indices as seen from various spatial factors identified and analyzed having varying coefficients from which a predictive rental house price formula is generated. Maps were generated showing relationship of spatial factors with rental house prices. Investors or agencies can know the influence of the factors and may peg their decisions on the results of this research. This research is paramount in decision making procedures of investors as they eye on setting up developments within the constituency. The county government may benefit heavily as they may be able to work on certain areas that may increase revenue in the sector.

Keywords

real estate, rental houses, GIS, spatial hedonic model

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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