Parametric and Non-parametric Methods in Mass Appraisal on Poorly Developed Real Estate Markets*

Gnat, Sebastian; Doszyń, Mariusz

doi:10.35808/ersj/1740

Cited by 9 publications

(7 citation statements)

References 21 publications

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“…Whilst ML models generally perform better than MLR since they can identify complex relationships and do not suffer from overfitting or multicollinearity, where data is limited, they can be outperformed simply due to insufficient data to make precise predictions. These results are also supported by Gnat & Doszyn's findings which were discussed previously [12]. Due to the size of the Paphos market, and the accuracy provided regarding long-term rental properties, acquiring sufficient data to achieve higher-performing ML models would be difficult since through extensive market research almost 50% of the properties advertised were not suitable.…”

Section: Optimal Strategysupporting

confidence: 77%

“…Further to this, Limsombunchai, Gan, & Lee were able to achieve an R-Squared value of 0.75 with only 200 data points which demonstrates that even smaller sample sizes can be used to effectively create MLR models [11]. Interestingly, a study carried out that investigated developing markets with limited data found that MLR models outperformed both k-NN and RF regarding the mean absolute percentage errors (MAPE) [12]. The study used 318 data points from Szczecin in Poland, which is far less developed than Paphos and does not attract the same level of tourism.…”

Section: Data Collectionmentioning

confidence: 84%

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Factors Influencing Rental Investments in Paphos, Cyprus: Comparing Short and Long-Term Rental Strategies

Martin,

Dimopoulos,

Katafygiotou

2024

Preprint

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Understanding the optimal strategy for a real estate investment and how performance changes based on characteristics is crucial for optimising the achievable return. This is prominent in tourist areas such as Paphos where there is no clear distinction as to whether short or long-term approaches are optimal. The study aimed to develop a model for predicting the optimal rental strategy whilst assessing which model performed best and which property attributes impacted its return the greatest. Short-term data was collected from AirDNA and long-term data was manually collected from real estate agents websites. Furthermore, Random Forest, K-Nearest Neighbour and Multiple Linear Regression models were created to predict the highest and best use for each property. Model accuracy varied between data sets with the best performing model for short-term properties being Random Forest (R-Squared: 0.843), and Distance-Based Multiple Linear Regression for long-term approach (R-Squared: 0.843). The study demonstrated that accurate models could be created to predict the optimal rental strategy with the number of bedrooms being the main driver for rental income, followed by luxury finishes and the presence of a pool. It found that locational characteristics didn’t impact the returns significantly assuming that the property was located within a tourist area.

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Section: Optimal Strategysupporting

confidence: 77%

Section: Data Collectionmentioning

confidence: 84%

Factors Influencing Rental Investments in Paphos, Cyprus: Comparing Short and Long-Term Rental Strategies

Martin,

Dimopoulos,

Katafygiotou

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Furthermore, Limsombunchai, Gan, and Lee were able to achieve an Rsquared value of 0.75 with only 200 data points, which demonstrates that even smaller sample sizes can be used to effectively create MLR models [25]. Interestingly, one study that investigated developing markets with limited data found that MLR models outperformed both the k-NN and RF methods regarding their mean absolute percentage errors (MAPEs) [26]. The study used 318 data points from Szczecin in Poland, which is far less developed than Paphos and does not attract the same level of tourism.…”

Section: Data Collectionmentioning

confidence: 98%

Factors Influencing Rental Investments in Paphos, Cyprus: Comparing Short- and Long-Term Rental Strategies

Martin,

Dimopoulos,

Katafygiotou

2024

Real Estate

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Understanding the optimal strategy for a real-estate investment and how performance changes based on characteristics is crucial for optimising the achievable return. This is prominent in touristic areas such as Paphos, Cyprus, where there is no clear distinction as to whether short- or long-term approaches are optimal. This study aimed to develop a model for predicting the optimal rental strategy whilst assessing which model performed best and which property attributes impacted its return the greatest. Short-term data were collected from AirDNA and long-term data were manually collected from real-estate agents’ websites. Furthermore, Random Forest, K-Nearest Neighbour, and Multiple Linear Regression models were created to predict the highest and best use for each property. Model accuracy varied between datasets, with the best-performing model for short-term properties being the Random Forest model (R-squared: 0.843), and the distance-based Multiple Linear Regression approach being the best for long-term properties (R-squared: 0.843). The study demonstrated that accurate models could be created to predict the optimal rental strategy with the number of bedrooms being the main driver for rental income, followed by luxury finishes and the presence of a pool. It was found that locational characteristics did not impact the returns significantly when assuming that the property was located within a touristic area.

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“…Ridge regression in real estate appraisal models was considered in (Newell, 1982;Anderson, 1981;Moore et al, 1984). The application of multiple regression and machine learning methods in real estate mass appraisal was presented in (Gnat & Doszyń, 2020). In this article, the regularization of ridge regression was also applied.…”

Section: Literature Reviewmentioning

confidence: 99%

Might expert knowledge improve econometric real estate mass appraisal?

Doszyń

2022

J Real Estate Finan Econ

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The article examines whether expert knowledge improves the estimation results of real estate mass appraisal models. Six econometric models were compared: OLS, mixed, the Bayesian model, the Inequality Restricted Least Squares (IRLS) model, ridge and LASSO regression (with regularization). In three of the models (mixed, Bayesian, and IRLS) prior knowledge was applied. In mixed and Bayesian models priors took the form of intervals for model parameters. In IRLS, restrictions in the form of inequalities were applied. In the empirical example mass appraisal models were applied in the valuation of undeveloped land for residential purposes. Models with prior knowledge turned out to be the best with regard to the consistency of estimates with theory. Also, prediction accuracy was better for models with prior knowledge. In the case of low quality data expert knowledge might significantly improve estimation results of real estate mass appraisal econometric models.

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Parametric and Non-parametric Methods in Mass Appraisal on Poorly Developed Real Estate Markets*

Cited by 9 publications

References 21 publications

Factors Influencing Rental Investments in Paphos, Cyprus: Comparing Short and Long-Term Rental Strategies

Factors Influencing Rental Investments in Paphos, Cyprus: Comparing Short and Long-Term Rental Strategies

Factors Influencing Rental Investments in Paphos, Cyprus: Comparing Short- and Long-Term Rental Strategies

Might expert knowledge improve econometric real estate mass appraisal?

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