Construction of house price indices in Dar es Salaam
Suggestion of a practical model for Tanzania amid data constraints
Time: Mon 2025-05-26 10.00
Video link: https://us02web.zoom.us/j/83183762796?pwd=dAoGl3cnyIg1f9plRHYj1Pcb3jiTwn.1
Language: English
Subject area: Real Estate and Construction Management
Doctoral student: Frank Nyanda , Fastigheter och byggande, Ardhi University (ARU), Dar Es Salaam, Tanzania
Opponent: Docent Peter Palm, Malmö universitet
Supervisor: Professor Mats Wilhelmsson, Fastighetsekonomi och finans; Dr Vianey Mushi, Ardhi University (ARU), Dar Es Salaam, Tanzania
QC 20250507
Abstract
Real estate significantly influences economic growth, with prices shaped by utility attributes and buyer willingness, making price dynamics crucial for stakeholders. In nascent real estate markets like Dar es Salaam, where data is less integrated and transactions often involve informal agents, creating accurate price indices is challenging, and methodologies may need to incorporate both formal and informal data sources, potentially with the help of machine learning techniques to improve predictions. Nevertheless, the Dar es Salaam housing market lacks indices, despite the existence of data sources, particularly formal and informal real estate agents. The main objective of this doctoral thesis is to examine the adoption of the best method for developing a house price index (HPI) for Dar es Salaam, Tanzania's most active real estate submarket, which shares operational characteristics with other regional submarkets in the country.
This thesis consists of four papers, utilising a survey strategy and cross-sectional data from real estate agents. It examines the feasibility of using informal real estate agents' data to establish a house price index in Dar es Salaam, the impact of spatial dependence on the index, the impact of informal and formal agents' data sources on the index and the use of machine learning techniques for property valuation, aiming to highlight its feasibility for house pricing.
The findings of the study indicate that the hedonic approach, with the informal agents’ data, appears to yield a useful house price index that shows a steady but rising trend (paper I). The hedonic pricing model for Dar es Salaam may not require spatial considerations due to data limitations, suggesting that proximity factors and spatial dependence may not significantly improve the house price index (paper II). Since the resulting price trend seems to be consistent with both formal and informal real estate agents, the house price index can be constructed using data from both sources. Nevertheless, incorporating data from various agent categories improves the index, likely due to the larger sample size (paper III). Despite challenges with informal market data, machine learning techniques can effectively estimate housing worth, with some methods consistently outperforming others (paper IV).
The study poses several implications for various stakeholders. The hedonic modelling approach is effective for developing house price indices in Dar es Salaam's nascent housing market. Policies must encourage informal agents to share their property transaction data. This could be through mandating the digitisation of informal transactions. Policies should also encourage standardised data formats and reporting for both formal and informal housing transactions to ensure consistency and reliability in integrating datasets into machine learning models. Data privacy regulations must ensure secure and ethical handling of sensitive information from individuals and informal agents.