1# RECORD b3911e100e43278a0d85948b17dd0e029042bb652# TITLE: Analysis of Spatial Factors Affecting Rental House Prices: A Case Study of Nyeri Town Constituency, Kenya3# YEAR: 2021.0 VENUE: Journal of Geosciences and Geomatics DOI: 10.12691/jgg-9-3-24# AUTHORS: E. N. Kimani; B. Kuria; M. M. Ngigi5# PAGES: 14 CANDIDATE TABLE PAGES (1-based): [12]6# 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.789===== PAGE 1 (score 6, numeric-paren 0, table-words 0, var-words 6) =====1011Journal of Geosciences and Geomatics, 2021, Vol. 9, No. 3, 110-123 12Available online at http://pubs.sciepub.com/jgg/9/3/2 13Published by Science and Education Publishing 14DOI:10.12691/jgg-9-3-2 15Analysis of Spatial Factors Affecting Rental House 16Prices: A Case Study of Nyeri Town Constituency, 17Kenya 18Evanson Ndung’u Kimani*, Bartholomew Thiong’o Kuria, Moses Murimi Ngigi 19Institute of Geomatics, GIS and Remote Sensing, Dedan Kimathi University of Technology, 20Private Bag-10143, Dedan Kimathi, Nyeri, Kenya 21*Corresponding author: 22 23Received May 17, 2021; Revised June 21, 2021; Accepted July 01, 2021 24Abstract Rental properties transactions have steadily increased annually by 2.7% globally since 2017. For a 25developing country like Kenya the transactions contributes 8.1% of Kenya’s gross domestic product according to 262019 national accounts statistics as it forms the basis of basic human need, shelter. It is projected to increase to 22% 27by 2020 in line with Government’s ‘big four’ agenda among them affordable housing, thus attracting investors 28constructing both commercial and residential houses. Investors in the real estate sector use a range of methods to 29determine rental house prices. Diverse factors have been noted to influence rental house prices some having higher 30effect than others. This research endeavors to analyze the spatial factors that affect rental houses prices in Nyeri 31town constituency, Kenya and determine their relationship to their current respective prices. Primary data was 32collected from rental house owners and assigned managing estate agents via structured questionnaires distributed 33through purposive sampling method. Guided by literature and data collected, roads, land value, houses and 34population data alongside secondary data was acquired and analyzed using the spatial hedonic model. Through GIS 35technologies, spatial factors identified from the data collected affecting rental house prices are analyzed and their 36relationship with rental house prices was determined. Regression and multicriteria analysis assigned different 37weights to the various factors as they were noted to influence the prices differently. From analytic hierarchy process, 38varying percentages were deduced on all the factors. Study results indicates different indices as seen from various 39spatial factors identified and analyzed having varying coefficients from which a predictive rental house price 40formula is generated. Maps were generated showing relationship of spatial factors with rental house prices. Investors 41or agencies can know the influence of the factors and may peg their decisions on the results of this research. This 42research is paramount in decision making procedures of investors as they eye on setting up developments within the 43constituency. The county government may benefit heavily as they may be able to work on certain areas that may 44increase revenue in the sector. 45Keywords: real estate, rental houses, GIS, spatial hedonic model 46Cite This Article: Evanson Ndung’u Kimani, Bartholomew Thiong’o Kuria, and Moses Murimi Ngigi, 47“Analysis of Spatial Factors Affecting Rental House Prices: A Case Study of Nyeri Town Constituency, Kenya.” 48Journal of Geosciences and Geomatics, vol. 9, no. 3 (2021): 110-123. doi: 10.12691/jgg-9-3-2. 491. Introduction 50Every investment is in some way or another dependent 51on land and property [1]. Housing being a basic need has 52attracted 22.8% of Kenya’s real estate developers to offer 53safe and comfortable living spaces for dwellers [2]. It has 54translated to a booming business in most parts of the 55country leading to 80 million dollars income on rental 56properties collected from the sector by the Kenya Revenue 57Authority [3]. This is by the close of the 2018/2019 58financial year having a notable increase of 42% revenue 59growth [4] from the close of 2015/2016 financial year. 60Real estate sector has experienced significant growth in 61the last decade [5]. Kenya has attracted worldwide 62investors who set up residential and commercial 63apartments leading to a 4.2% increase in gross domestic 64product in 2019 [4]. The Kenyan government under its 65four pillar agenda whereby housing is one of them plans to 66offer 500,000 affordable housing to all Kenyans across the 6747 counties by 2022 [6,7]. Real estate remains to be one of 68the market leaders contributing 8.1% (2019) of Kenya’s 69gross domestic product [8]. 70Determining appropriate rental house prices has been a 71major challenge due to lack of rental houses pricing 72criteria [9]. Property valuation reports indicate that many 73house owners have been either undercharging or 74overcharging. In other cases there have been varying 75prices for houses of similar nature and quality. Investors 76and house owners lack a tool that guide them towards 77their pricing criteria same case to house seekers who fall 78 798081===== PAGE 12 (score 20, numeric-paren 0, table-words 3, var-words 9) =====8283121 84Journal of Geosciences and Geomatics 85 863.2. Combined Rental House Price Predictive 87Formula 88Rental house predictive formula was generated after 89consideration of all the factors. Different weights was 90assigned through a regression analysis of all the different 91types of houses identified within the area as demonstrated. 92Rental house predictive formulae: 931. Rent= 0.0002(Land Value) + 5360.7 942. Rent= -7.1833(pop density) + 10327 953. Rent= 963.46 (security) + 5746.6 964. Rent= 52.531(slope) + 6844.4 975. Rent= -1.5174(towns) + 11095 986. Rent= 0.0988 (floor size) + 209.12 997. Rent= -0.1726(dist_international) + 8324.3 1008. Rent=-0.6566 (dist_national) + 8204.9 1019. Rent=-0.3707(dist_primary) + 8240.4 10210. Rent= -0.8707(dist_secondary) + 9118.7 103Rent/10 = 73471.12 + 0.0002(Land Value) -7.1833 104(pop density) + 963.46 (security) + 52.531 (slope) -1.5174 105(towns) +0.0988 (floor size) -0.1726 (dist_international) -1060.6566 (dist_national) -0.3707 (dist_primary) -0.8707 107(dist_secondary) 1083.2.1. Multicriteria Analysis 109From the result of multi criteria analysis, the factors 110examined were noted to affect the rental house price with 111varying percentages (Table 3). Analytic Hierarchy Process 112(AHP) is based on a hierarchical structuring of the 113elements that are involved in a decision problem. The 114evaluation of the hierarchy is based on pairwise 115comparisons. 116The Decision Maker’s (DM’s) judgment may not be 117consistent with one another, so AHP measures the 118inconsistency of judgments by calculating the consistency 119index CI of the matrix. The CI is in turn divided by the 120average random consistency index RI to obtain the 121consistency ratio CR. The RI index is a constant value for 122an n × n matrix, which has resulted from a computer 123simulation of matrices with random values from the 0- 8. 124Table 3. Percentage influence of the factors affecting rental house 125prices 126CLASSES 127% 128Dist to Towns 12911.93 130Dist to Roads 13110.09 132Floor size 13333.81 134Land Value 13519.27 136Pop Density 1374.73 138Security 13912.97 140Slope 1417.22 1423.2.2. Final Combined Spatial Hedonic Rental Houses 143Predictive Formula 144With the application of spatial hedonic model having 145incorporated both spatial and non-spatial factors, the 146resultant combined hedonic regression equation is 147therefore: 148RENTAL HOUSE PRICE = 73471.12 + 0.0002(Land 149Value) -7.1833 (pop density) + 963.46 (security) + 15052.531 (slope) -1.5174 (towns) +0.0988 (floor size) -1510.1726 (dist_international) -0.6566 (Dist_National) -1520.3707 (dist_primary) -0.8707 (dist_secondary) - 1530.02(Noise) + 0.094(Scenic Beauty) + 0.053(tiles) + 1540.0001(Fence) + 0.025 (Garage) - 0.050(Pollution) - 1550.074(Night Guard) + 0.0001(Parking) – 0.0003(House 156Age) 1573.3. Model Validation 158With a total of 50 houses gathered from the second 159most developed constituency in Nyeri County, Mathira 160Constituency, validation data was prepared and analysed. 161The data was overlaid in Google earth pro for 162visualization with the combine spatial model. Rental 163values from the model were extracted into the collected 164points and exported in an excel spreadsheet. A correlation 165between the house rent and collected data rent values gave 166a mean R2 of 0.9767 from the statistical description 167(Figure 14). 168 169Figure 14. Model validation 170R² = 0.97617101725000173100001741500017520000176250001770178500017910000180150001812000018225000183Rent - Prediction Model184Rent Grouth truth185Model Validation186 187188189===== PAGE 13 (score 11, numeric-paren 0, table-words 0, var-words 11) =====190191 192Journal of Geosciences and Geomatics 193122 1944. Conclusion 195The spatial factors which were noted to influence rental 196house prices include; land Value, population density, 197proximity to towns, slope, security and distance to roads. 198Regression analysis and spatial hedonic modelling are 199valuable tools for real estate professionals in determining 200the correlation between building characteristics and the 201transaction price, as well as to predict future transaction 202pricing. Using these techniques can facilitate building 203valuation when traditional discounted cash flow models 204cannot be populated, which can often be the case if an 205asset is located in a remote location and/ or its structure is 206unique to other building assets. The results from this study 207can also produce answers to development decisions such 208as what building attributes to include in an effort to 209generate the highest value on a parcel of land. 210GIS and real estate technologies have proven to be 211powerful tools in the analysis of real estate dynamic 212market if well adopted. With well laid down framework, 213decision making tools can be made which will enable 214users to make informed decisions in regards to possible 215investments and house type well knowing the various 216factors that will affect prices and how. More research 217should go to development of predictive models. 218Acknowledgements 219I would like to express my sincere gratitude to the 220ministry of land Nyeri for providing me with the relevant 221data including mass valuation rolls for the implementation 222of this project. Special thanks goes to Mr. F.M.Mutegi 223Nyeri county valuer for his support. 224My thanks and appreciation also go to all the house 225owners and estate agents who gave out information via 226questionnaires and one on one interviews. 227References 228[1] 229E. 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