Nonparametric Estimation of A Hedonic Price Model: A South African Case Study

Preez, Mario Du; Sale, M.C.

doi:10.1080/10800379.2013.12097253

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…First, we estimated a linear parametric technique via OLS method. Parametric techniques are the most widely used techniques in estimating the hedonic price function (Preez et al , 2013). Second, we applied semiparametric regression because the relationship between the logarithm of selling price and apartment characteristics is not necessarily linear.…”

Section: Methodology and Datamentioning

confidence: 99%

Landscape view, height preferences and apartment prices: evidence from major urban areas in Indonesia

Hasanah

Yudhistira

2018

IJHMA

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Purpose Landscape view is a crucial factor in house-buying decisions. Landscape views provide an amenity to residents, and this can influence the house or apartment owners in their residence decisions. Yet, the relative value of different types of view potentially differs. Additionally, the value of each type of view may differ depending on an apartment’s elevation above the ground level. In this study, the authors aim to estimate the value of landscape views on apartment prices in major urban areas in Indonesia. Design/methodology/approach This paper evaluates to what extent various landscape views including mountain, sea, river, lake, street, urban village, garden and sports center views affect apartment prices in major urban areas in Indonesia. Two hedonic regression approaches are used: ordinary least squares and semiparametric regression. The latter is used to accommodate a possible non-linearity in the relationship between price and apartment characteristics. The model also incorporates housing and locational characteristics as control variables. Findings Using online apartment market data, the estimates in this paper show some degree of heterogeneity in the value of various views to the extent of providing negative externalities. Mountain, street and sports center views are associated with higher apartment prices. Sea, lake and garden views are statistically insignificant in explaining the prices. In contrast, the unappealing nature of the rivers and their surrounding creates a negative impact on prices. The estimates also suggest that an apartment’s floor height plays a significant role in the valuation of views. Originality/value There is little research on landscape view effects on apartment prices, especially in Indonesia. In addition, the relationship between the value of views and height preferences has seldom been analyzed. This paper provides the valuation of an extensive list of landscape views in urban areas in Indonesia. The estimation results also suggest that the value of views may differ depending on the floor on which an apartment lies.

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Section: Methodology and Datamentioning

confidence: 99%

Landscape view, height preferences and apartment prices: evidence from major urban areas in Indonesia

Hasanah

Yudhistira

2018

IJHMA

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“…This means that the NIMBY unfavourable effect on houses is confirmed if houses in proximity to an MIH experience decline in prices. Literature specified hedonic price modelling with the use of other control attributes and characteristics into environmental, structural, neighbourhood and locational factors (Boshoff, 2013;Du Preez, 2013;Priilaid, D, & van Rensburg, 2012;Borowiecki, 2009;Ham & Manley, 2009;Selim, 2008;Delorenzi, 2006;Garner et al 2006;Van Wen et al 2004;Limsombunc et al 2004;Barker, 2003;Ball, 1973). Also, distance contours are able to capture proximity advantages in determining house prices.…”

Section: Location Theorymentioning

confidence: 99%

“…Hedonic theory maintains that a house price for each house sold (Pi) can be deconstructed into its attributes/characteristics, implicit prices that include Structural Attributes (SA), Locational Attributes (LA), and Neighbourhood Attributes (NA), Income categories (Y) and Race categories (R) (Ham, 2011;Du Preez et al 2013). These include crime rate, income groups, number of bedrooms, number of bathrooms, distance to the Central Business District, distance to the shopping malls, race type, age and distance to informal settlement.…”

Section: Cosmo City Mih Neighbourhood Hedonic Variablesmentioning

confidence: 99%

Does an Inclusive Housing Development Depress Neighbourhood House Prices? A Case Study of Cosmo City, Johannesburg, South Africa.

SIMBANEGAVI¹,

IJASAN²

2022

JICBE

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South Africa joined the global world in building inclusive housing, known as Mixed Income Housing (MIH) as a way to integrate and transform residential markets previously distorted by the discriminatory apartheid regime. However, despite the benefits of MIHs in deconcentrating poverty while boosting housing supply, these inclusive neighbourhoods often experience amplified cases of ‘Not in My Back Yard’ (NIMBY) for various reasons. Thus, approval processes of these inclusive housing developments get to be highly contested in courts, which causes huge delays in their completion. The paper aims to investigate the effect of NIMBY on the price of houses in a mixed-income neighbourhood. The paper used a cross sectional hedonic model on houses sold in the neighbourhood of Cosmo City MIH. The limitation is that a typical and purposely selected Cosmo city case study may not be generalizable to South Africa at large. Results show that Cosmo City had negligible effects on neighbourhood house prices. This is rather surprising given the unfavourable perception encountered during its development. The practical implication is that improving infrastructure such as roads to reduce traffic congestion, building new schools, new hospitals, security services, and new shopping centers reduce pressure on available services and amenities making inclusive housing acceptable in its neighbourhood. The social implication is that inclusive housing developments default into supplying the much-needed social housing in South Africa. Scientifically measuring perception on accepting MIH development projects in well-established neighbourhoods does contribute to understanding the plight of housing shortage by the public in ways that accepts inclusivity from an investment point of view.

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“…This led the researchers to develop a quantile regression model where they found the model coefficients varied across quantiles, indicating that hedonic prices were sensitive across the price distribution. Du Preez, et al (2013) developed a hedonic price function for houses in Walmer, South Africa, using the local constant estimator where the direct estimate of 𝐸(𝑦|𝑥) is produced with a kernel function that produces a smooth estimate of the densities. The researchers found that this nonparametric technique outperformed the parametric linear model.…”

Section: Background and Objectivementioning

confidence: 99%

Machine Learning Web Application to Estimate Listing Prices of South African Homes

Bax¹,

Zewotir²,

North³

2020

JARER

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Due to the heterogeneous nature of residential properties, determining selling prices which will reconcile supply and demand is difficult. Establishing realistic listing prices is vitally important for sellers to prevent prolonged time on market. Sellers have several resources available to assist in this endeavour, all of which involve understanding current market dynamics through analysing recent sales and listing data. Property portals which aggregate real estate agencies’ data, hosting it on online platforms, are one such resource, along with individual real estate agencies. Leveraging this data to develop solutions that could aid sellers in listing price decision making is a potential business objective that could not only add value to sellers but create a competitive advantage by increasing traffic to an online real estate platform. Using data provided by a South African online property portal, this paper creates a web application using machine learning to estimate listing prices for different types of homes throughout South Africa. This study compared log linear and gradient boosted models, estimating residential listing prices over a four-year period. The results indicate that although log linear models are suitable to account for spatial dependency in the data through the inclusion of a fixed location effect, the assumption of linear functional form was not satisfied. The gradient boosted models do not impose explicit functional form requirements, making them flexible candidates. Similarly, these models were able to handle the spatial dependency adequately. The gradient boosted models also achieved a lower out of sample error compared to the log linear models. The findings show that over observation periodperiod, larger properties consistently experience a diminishing return at some point over the marginal distribution of physical characteristics. The web application details how sellers are easily able to obtain mean listing price estimates and gauge the growth thereof, by simply inputting their property interest criteria.

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Nonparametric Estimation of A Hedonic Price Model: A South African Case Study

Cited by 6 publications

References 23 publications

Landscape view, height preferences and apartment prices: evidence from major urban areas in Indonesia

Landscape view, height preferences and apartment prices: evidence from major urban areas in Indonesia

Does an Inclusive Housing Development Depress Neighbourhood House Prices? A Case Study of Cosmo City, Johannesburg, South Africa.

Machine Learning Web Application to Estimate Listing Prices of South African Homes

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