The role of spatial and temporal structure for residential rent predictions

Füss, Roland; Koller, Jan

doi:10.1016/j.ijforecast.2016.06.001

Cited by 23 publications

(22 citation statements)

References 41 publications

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“…For comparison, if the listing price and the out of sample predictions from the PBA model here are logged then the r 2 increases from 0.50 to 0.76, still below Chung’s 0.98 value. Other reported r 2 on the log scale are 0.329 and 0.315 in Appendix A of Banzhaf and Farooque [22]; 0.854 and 0.856 in Table 6 of Löchl [59]; Fuss and Koller [25] quote 0.883 (Table 3, STAR model), finally Baron and Kaplan [26] report 0.753 in Table 3. On the untransformed scale, Prunty [29] reports R 2 of 0.19 for his California model and 0.13 for his New York model, Table 11; much higher R 2 values of 0.607 and 0.622 are reported by McCord, Davis [30] in Table V. Clearly, even in this limited number of studies the range of R 2 values is wide but these results sit comfortably within this range.…”

Section: Discussion and Evaluationmentioning

confidence: 99%

“…Firstly there are thresholds to set for how far and how long ago rental listings should be used. Fuss and Koller [25] experiment with time windows of 400, 500 and 600 days and whilst there are differences in their model RMSEs, they are not great. Similar experiments with distance lags also suggest that their spatial models are largely invariant to these choices.…”

Section: Case Descriptionmentioning

confidence: 99%

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A mass-market appraisal of the English housing rental market using a diverse range of modelling techniques

Clark

Lomax

2018

J Big Data

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Introduction Mass appraisals in the rental housing market are far less common than those in the sales market. However, there is evidence for substantial growth in the rental market and this lack of insight hampers commercial organisations and local and national governments in understanding this market. Case description This case study uses data that are supplied from a property listings web site and are unique in their scale, with over 1.2 million rental property listings available over a 2 year period. The data is analysed in a large data institute using generalised linear regression, machine learning and a pseudo practitioner based approach. Discussion and evaluation The study should be seen as a practical guide for property professionals and academics wishing to undertake such appraisals and looking for guidance on the best methods to use. It also provides insight into the property characteristics which most influence rental listing price. Conclusions From the regression analysis, attributes that increase the rental listing price are: the number of rooms in the property, proximity to central London and to railway stations, being located in more affluent neighbourhoods and being close to local amenities and better performing schools. Of the machine learning algorithms used, the two tree based approaches were seen to outperform the regression based approaches. In terms of a simple measure of the median appraisal error, a practitioner based approach is seen to outperform the modelling approaches. A practical finding is that the application of sophisticated machine learning algorithms to big data is still a challenge for modern desktop PCs.

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Section: Discussion and Evaluationmentioning

confidence: 99%

Section: Case Descriptionmentioning

confidence: 99%

A mass-market appraisal of the English housing rental market using a diverse range of modelling techniques

Clark

Lomax

2018

J Big Data

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“…Spatial heterogeneity in hedonic pricing models has at least two different aspects. First, the heterogeneous structure of residuals leads to inconsistent estimates of pricing coefficients (see, e.g., Füss and Koller (2016)). Second, the residuals can be regarded as the price of unobserved property factors.…”

Section: Estimation Strategymentioning

confidence: 99%

Best Land Use with Negative Externalities: Determining Land Values from Residential Rents

FFss,

Koller

2017

SSRN Journal

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When land regulations are binding, then the land price per m 2 is determined by the attractiveness of the location and the restrictiveness of the regulation. In the case of a maximum floor area ratio (F AR max) restriction, the best use land price can be directly expressed as a function of the F AR max and local amenities. We show theoretically and empirically how this approach can be used to determine land values from residential rents. From our empirical results, we derive two main sources for a monocentric structure of land prices. First, the location attractiveness of centrally located dwellings makes land prices more expensive. Second, on a regulatory basis, the F AR max works as a multiplier for land prices. Because the F AR max is high in central areas, land prices are inflated accordingly. Our model gives insight into determinants of urban land prices. In addition, it is a useful approach for land appraisal in urban regions.

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“…At present, a number of automated systems are presented in the professional literature [6][7][8]. In most cases, however, these are systems for valuing apartments [9,10].…”

Section: Introductionmentioning

confidence: 99%

Possibilities of House Valuation Automation in the Czech Republic

2020

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Valuation of single-family detached houses is necessary when determining the amounts of some taxes. The current systems of the Czech Republic are outdated in this respect, and they are based on procedures used in the 1980s. The values found do not correspond to current market conditions very often. This article attempts to verify the applicability of a methodology where the value of a detached house is decomposed into the value of the land and the value of the object as such when considering wear. For verification, 122 sales of detached houses in Ostrava and its surroundings were analyzed, and the results show that the values determined by the verified methodology do not differ by more than 10% from the actual sales prices in most cases. The methodology is very simple and practically applicable for users without deep knowledge of construction or valuation principles. It can be applied, for example, when calculating the bases of certain taxes or as an indicative guide for the pricing of real estate for sellers, buyers, real estate agents, etc.

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The role of spatial and temporal structure for residential rent predictions

Cited by 23 publications

References 41 publications

A mass-market appraisal of the English housing rental market using a diverse range of modelling techniques

A mass-market appraisal of the English housing rental market using a diverse range of modelling techniques

Best Land Use with Negative Externalities: Determining Land Values from Residential Rents

Possibilities of House Valuation Automation in the Czech Republic

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