# RECORD b3911e100e43278a0d85948b17dd0e029042bb65 # TITLE: Analysis of Spatial Factors Affecting Rental House Prices: A Case Study of Nyeri Town Constituency, Kenya # YEAR: 2021.0 VENUE: Journal of Geosciences and Geomatics DOI: 10.12691/jgg-9-3-2 # AUTHORS: E. N. Kimani; B. Kuria; M. M. Ngigi # PAGES: 14 CANDIDATE TABLE PAGES (1-based): [12] # 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. ===== PAGE 1 (score 6, numeric-paren 0, table-words 0, var-words 6) ===== Journal of Geosciences and Geomatics, 2021, Vol. 9, No. 3, 110-123 Available online at http://pubs.sciepub.com/jgg/9/3/2 Published by Science and Education Publishing DOI:10.12691/jgg-9-3-2 Analysis of Spatial Factors Affecting Rental House Prices: A Case Study of Nyeri Town Constituency, Kenya Evanson Ndung’u Kimani*, Bartholomew Thiong’o Kuria, Moses Murimi Ngigi Institute of Geomatics, GIS and Remote Sensing, Dedan Kimathi University of Technology, Private Bag-10143, Dedan Kimathi, Nyeri, Kenya *Corresponding author: Received May 17, 2021; Revised June 21, 2021; Accepted July 01, 2021 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 Cite This Article: 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, vol. 9, no. 3 (2021): 110-123. doi: 10.12691/jgg-9-3-2. 1. Introduction Every investment is in some way or another dependent on land and property [1]. Housing being a basic need has attracted 22.8% of Kenya’s real estate developers to offer safe and comfortable living spaces for dwellers [2]. It has translated to a booming business in most parts of the country leading to 80 million dollars income on rental properties collected from the sector by the Kenya Revenue Authority [3]. This is by the close of the 2018/2019 financial year having a notable increase of 42% revenue growth [4] from the close of 2015/2016 financial year. Real estate sector has experienced significant growth in the last decade [5]. Kenya has attracted worldwide investors who set up residential and commercial apartments leading to a 4.2% increase in gross domestic product in 2019 [4]. The Kenyan government under its four pillar agenda whereby housing is one of them plans to offer 500,000 affordable housing to all Kenyans across the 47 counties by 2022 [6,7]. Real estate remains to be one of the market leaders contributing 8.1% (2019) of Kenya’s gross domestic product [8]. Determining appropriate rental house prices has been a major challenge due to lack of rental houses pricing criteria [9]. Property valuation reports indicate that many house owners have been either undercharging or overcharging. In other cases there have been varying prices for houses of similar nature and quality. Investors and house owners lack a tool that guide them towards their pricing criteria same case to house seekers who fall ===== PAGE 12 (score 20, numeric-paren 0, table-words 3, var-words 9) ===== 121 Journal of Geosciences and Geomatics 3.2. Combined Rental House Price Predictive Formula Rental house predictive formula was generated after consideration of all the factors. Different weights was assigned through a regression analysis of all the different types of houses identified within the area as demonstrated. Rental house predictive formulae: 1. Rent= 0.0002(Land Value) + 5360.7 2. Rent= -7.1833(pop density) + 10327 3. Rent= 963.46 (security) + 5746.6 4. Rent= 52.531(slope) + 6844.4 5. Rent= -1.5174(towns) + 11095 6. Rent= 0.0988 (floor size) + 209.12 7. Rent= -0.1726(dist_international) + 8324.3 8. Rent=-0.6566 (dist_national) + 8204.9 9. Rent=-0.3707(dist_primary) + 8240.4 10. Rent= -0.8707(dist_secondary) + 9118.7 Rent/10 = 73471.12 + 0.0002(Land Value) -7.1833 (pop density) + 963.46 (security) + 52.531 (slope) -1.5174 (towns) +0.0988 (floor size) -0.1726 (dist_international) - 0.6566 (dist_national) -0.3707 (dist_primary) -0.8707 (dist_secondary) 3.2.1. Multicriteria Analysis From the result of multi criteria analysis, the factors examined were noted to affect the rental house price with varying percentages (Table 3). Analytic Hierarchy Process (AHP) is based on a hierarchical structuring of the elements that are involved in a decision problem. The evaluation of the hierarchy is based on pairwise comparisons. The Decision Maker’s (DM’s) judgment may not be consistent with one another, so AHP measures the inconsistency of judgments by calculating the consistency index CI of the matrix. The CI is in turn divided by the average random consistency index RI to obtain the consistency ratio CR. The RI index is a constant value for an n × n matrix, which has resulted from a computer simulation of matrices with random values from the 0- 8. Table 3. Percentage influence of the factors affecting rental house prices CLASSES % Dist to Towns 11.93 Dist to Roads 10.09 Floor size 33.81 Land Value 19.27 Pop Density 4.73 Security 12.97 Slope 7.22 3.2.2. Final Combined Spatial Hedonic Rental Houses Predictive Formula With the application of spatial hedonic model having incorporated both spatial and non-spatial factors, the resultant combined hedonic regression equation is therefore: RENTAL HOUSE PRICE = 73471.12 + 0.0002(Land Value) -7.1833 (pop density) + 963.46 (security) + 52.531 (slope) -1.5174 (towns) +0.0988 (floor size) - 0.1726 (dist_international) -0.6566 (Dist_National) - 0.3707 (dist_primary) -0.8707 (dist_secondary) - 0.02(Noise) + 0.094(Scenic Beauty) + 0.053(tiles) + 0.0001(Fence) + 0.025 (Garage) - 0.050(Pollution) - 0.074(Night Guard) + 0.0001(Parking) – 0.0003(House Age) 3.3. Model Validation With a total of 50 houses gathered from the second most developed constituency in Nyeri County, Mathira Constituency, validation data was prepared and analysed. The data was overlaid in Google earth pro for visualization with the combine spatial model. Rental values from the model were extracted into the collected points and exported in an excel spreadsheet. A correlation between the house rent and collected data rent values gave a mean R2 of 0.9767 from the statistical description (Figure 14). Figure 14. Model validation R² = 0.976 0 5000 10000 15000 20000 25000 0 5000 10000 15000 20000 25000 Rent - Prediction Model Rent Grouth truth Model Validation ===== PAGE 13 (score 11, numeric-paren 0, table-words 0, var-words 11) ===== Journal of Geosciences and Geomatics 122 4. Conclusion The spatial factors which were noted to influence rental house prices include; land Value, population density, proximity to towns, slope, security and distance to roads. Regression analysis and spatial hedonic modelling are valuable tools for real estate professionals in determining the correlation between building characteristics and the transaction price, as well as to predict future transaction pricing. Using these techniques can facilitate building valuation when traditional discounted cash flow models cannot be populated, which can often be the case if an asset is located in a remote location and/ or its structure is unique to other building assets. The results from this study can also produce answers to development decisions such as what building attributes to include in an effort to generate the highest value on a parcel of land. GIS and real estate technologies have proven to be powerful tools in the analysis of real estate dynamic market if well adopted. With well laid down framework, decision making tools can be made which will enable users to make informed decisions in regards to possible investments and house type well knowing the various factors that will affect prices and how. More research should go to development of predictive models. Acknowledgements I would like to express my sincere gratitude to the ministry of land Nyeri for providing me with the relevant data including mass valuation rolls for the implementation of this project. Special thanks goes to Mr. F.M.Mutegi Nyeri county valuer for his support. My thanks and appreciation also go to all the house owners and estate agents who gave out information via questionnaires and one on one interviews. References [1] E. Candas et al, “Determining the Factors Affecting Housing Prices,” p. 10, 2017. [2] “Property in Kenya - Rent & Buy Real Estate: HassConsult.” https://hassconsult.co.ke/real-estate/index.php?typ=2&layout=3 (accessed Mar. 10, 2020). [3] KRA, “Residential Rental Income Tax - KRA,” 2019. https://www.kra.go.ke/individual/filing-paying/types-of- taxes/residential-rental-income (accessed Nov. 25, 2018). [4] “Cytonn Home.” https://www.cytonn.com// (accessed Mar. 10, 2020). 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