Hedonic valuation of apartments in Kenya

Hoxha²,

2021

IJHMA

Purpose The purpose of this study is to identify which are the main factors influencing the apartment prices in Prishtina, the capital of Kosovo, during the period 2018–2020. The factors identified will be used by real estate developers and investors for better decision-making in apartment investments. Design/methodology/approach The research methodology is quantitative. The methodology analyzes 1,468 real estate transaction contracts of apartment buildings using a probability random sampling. The research methodology uses multiple regression analysis to identify whether the research model is significant in predicting apartment prices but also identify which are the main factors that influence the apartment prices in Prishtina, Kosovo. Findings The present study finds that location, size, floor, access to road and building quality affect apartment prices in Prishtina, whereas surprisingly access to green spaces and availability of parking spaces have no statistically significant effect on apartment prices in Kosovo. Research limitations/implications The study has great implications for the real estate developers in Prishtina, Kosovo showing how to improve their decision-making process in real estate investments to know which characteristics are most valued by investors for investment in new apartment buildings in Prishtina, Kosovo and local authorities in Prishtina, Kosovo to modernize the access to road infrastructure for its inhabitants and adopt building regulations that will enforce strict criteria as far as building quality is concerned. Originality/value The study is the first quantitative study that studies the factors influencing the apartment prices in Prishtina, Kosovo.

Section: Discussionsupporting

confidence: 82%

Section: Ijhma 155supporting

confidence: 82%

Study of factors influencing apartment prices in Prishtina, Kosovo

Hoxha²,

2021

IJHMA

“…Many studies discussed the construction of the housing price index and its implications for property management (Tse and Love, 2000; Roubi and Ghazaly, 2007; Benefield, 2009; Shimizu, 2014; Kurvinen and Tyvimaa, 2016; Kieti and Ogolla, 2021). Zhang and Yi (2017) constructed a quantile housing price index with a data set of newly built residential housing units in Beijing from 2013 to 2015.…”

Section: Literature Reviewmentioning

confidence: 99%

Constructing segmented rental housing indices: evidence from Beijing, China

Song

Wilhelmsson

Yang

2022

PurposeThis paper aims to construct rental housing indices and identify market segmentation for more effective property-management strategies.Design/methodology/approachThe hedonic model was employed to construct the rental indices. Using the k-means++ and REDCAP (Regionalisation with Dynamically Constrained Agglomerative Clustering and Partitioning) approaches, the authors conducted clustering analysis and identified different market segmentation. The empirical study relied on the database of 80,212 actual rental transactions in Beijing, China, spanning 2016–2018.FindingsRental housing market segmentation may distribute across administrative boundaries. Properly segmented indices could provide a better account for the heterogeneity and spatial continuity of rental housing and as well be crucial for effective property management.Research limitations/implicationsResidential rent might not only vary over space but also interplays with housing price. It would be worth studying how the rental market functions together with the owner-occupied sector in the future.Practical implicationsResidential rental indices are of great importance for policymakers to be able to evaluate housing policies and for property managers to implement competitive strategies in the rental market. Their constructions largely depend on the analysis of market segmentation, a trade-off between housing spatial heterogeneity and continuity.Originality/valueThis paper fills the gap in knowledge concerning segmented rental indices construction, particularly in China. The spatial constrained clustering approach (REDCAP) was also initially introduced to identify regionalised market segmentation due to its superior performance.

“…Some models have used few variables and others a substantial number of attributes, with implications on the final model developed. There is however a consensus that apartment values are influenced by locational, neighbourhood and structural attributes (Brett et al, 2003;Kieti and Ogolla, 2021;McCluskey and Borst, 2007;Ndegwa, 2018;Thanasi, 2016). Financial and economic variables have also been noted to be significant factors in time series models (Mbugua, 2014;Owusu-Ansah et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

“…These valuation approaches are based on the economic principles of price equilibrium, and the anticipation of benefits and substitution, respectively. The valuation methods have been broadly categorized into conventional/traditional (the sales comparison method, the income capitalization method, the contractor's method, the residual method and the profit method) and non-conventional/advanced methods (the artificial neural network, fuzzy logic, auto-regressive integrated moving average, spatial analysis, machine learning and hedonic pricing model) (Kieti and Ogolla, 2021). The conventional valuation methods have been observed to be easy to use, understand and interpret but low on reliability and accuracy, in comparison to the advanced ones (Zurada et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Modelling apartments values in the Nairobi metropolitan area, Kenya

Museleku

2022

PurposeThe purpose of this paper is to determine the most significant factors influencing apartment values in Nairobi Metropolitan Area (NMA), Kenya and develop a valuation model using the identified variables. The paper is thus promoting the adoption of advanced, more reliable and accurate valuation methods in developing economies such as Kenya. Therefore, the paper demonstrates the potential of the hedonic pricing method (HPM) in property valuation by making objective adjustments to comparable sales data.Design/methodology/approachThe paper utilizes a case study design by analysing recent (January 2020 to December 2021) apartment sales and apartment attributes in NMA. A sample size of 264 transactions was analysed using the HPM.FindingsThe study identified six significant factors affecting apartment values in the study area: apartment size, location, floor finishes, provision of shopping facilities, swimming pool and gym. A valuation model has been developed using the six variables.Research limitations/implicationsThe paper is contributing to the ongoing global debate on the accuracy, reliability and validity of property valuations with particular emphasis on the developing economies to adopt the more accurate, efficient and reliable valuation approaches.Practical implicationsThe findings are useful to property valuers and estate managers in determining the most statistically significant attributes affecting apartment values in the NMA hence enabling them to offer informed professional advice. However, the findings of the study are limited to the study area since real estate is heterogeneous.Social implicationsProperty investors, financiers and other stakeholders in the society will benefit from the findings of this study in their decision-making process. Additionally, wide adoption of the more reliable and accurate valuation approaches would foster public confidence in valuation and estate management professional services leading to enhanced consumption of these services by the society and promoting public care.Originality/valueThe paper is promoting valuation accuracy, efficiency and reliability in the developing economies by advocating for advanced valuation methods. It is the first attempt to develop a valuation model for apartments in the NMA, the capital city of Kenya and the business hub of East and Central Africa.