Search Results (13)

The prediction of real estate values is vital for taxation, transactions, mortgages, and urban policy development. Values can be predicted more accurately by statistical or advanced methods together when the size of the data is huge. In metropolitan cities like İstanbul, where size of the real estate data is vast and complex, mass appraisal methods supported by Machine Learning offer a scalable and consistent alternative. This study employs six algorithms: Artificial Neural Network, Extreme Gradient Boosting, K-Nearest Neighbors, Support Vector Regression, Random Forest, and Semi-Log Regression, to estimate the values of real estate on both the Asian and European continent parts of İstanbul. In total, 168,099 residential properties were utilized along with 30 of their features from both sides of the Bosphorus. The results show that RF yielded the best performance in Beşiktaş, while XGBoost performed best in Üsküdar. ANN also produced competitive results, although slightly less accurate than those of XGBoost and RF. In contrast, traditional SVR and SLR models underperformed, especially in terms of R² and RMSE values. With its large-scale dataset, focusing on one of the greatest metropolitan areas, Istanbul, and the usage of multiple ML algorithms, this study stands as a comprehensive and practical contribution to the field of automated real estate valuation. Full article

Price markups and firms’ Tobin’s Q ratios are widely believed to have been increasing in the past several decades. Various models for the calculation of price markups have been developed, each relying on the historically held definition of the ratio of price to marginal cost; however, all of these have methodological drawbacks, and some of the results they have produced have been poorly reflective of the near past wider macroeconomic experience. This paper defines a new approach for the definition and measurement of markup pricing, and it also avoids some of the issues surrounding the marginal cost approaches by using the measure of economic rent and the capital asset pricing model. The results show limited markup pricing for the UK’s FTSE 100 companies (2018–2023), but that certain real estate, technology/media and financial services/equity investment firms have enjoyed higher price markup levels. An analysis of the business models of these firms is used to qualitatively propose explanations for such markups. This work offers formal proof that that the expected price markup is equal to Tobin’s Q and finds that the empiric market level of markup is near equivalent to the market Tobin’s Q; the differences between the markup and Tobin’s Q at the level of the firm are equally assessed. This work challenges the general consensus that price markups are above one and have been increasing; it may also aid policy makers with respect to taxation policy and regulatory measures, as well as the financial management of firms in decisions concerning capital deployment and portfolio management. The method merits expansion to wider data sets, as well as to those from outside of the UK. Full article

Housing plays an essential role in sustainable governance due to its socio-economic and environmental connection. However, the relationship between governance policies, market behavior, and socio-economic outcomes varies geographically and demographically. Therefore, segregated policies developed and implemented may fail to achieve their desired objectives because of the sensitivity of housing policies for their connection to human wellbeing. The effectiveness of housing policies in geographically connected regions is one of the areas that has received little attention in the Canadian context. The study follows a multi-step empirical method using a multiple linear regression model and a difference-in-difference approach to assessing the geographical variation of speculation and property taxes on housing markets. The study confirms that speculation taxes are not an effective tool in curbing house prices. Similarly, considering the role of property taxes in providing public services, delinking property taxes from a potential contributor to house prices would provide a better lens to develop local housing policies. Furthermore, the study also confirms that the housing market can be better assessed at a local scale, considering geographical influence in conjunction with investment trends. Full article

With the rapidly increasing house prices in the Netherlands, there is a growing need for more localised value predictions for mortgage collaterals within the financial sector. Many existing studies focus on modelling house prices for an individual city; however, these models are often not interesting for mortgage lenders with assets spread out all over the country. That is why, with the current abundance of national geospatial datasets, this paper implements and compares three hedonic pricing models (linear regression, geographically weighted regression, and extreme gradient boosting—XGBoost) to model real estate appraisals values for five large municipalities in different parts of the Netherlands. The appraisal values used to train the model are provided by Stater N.V., which is the largest mortgage service provider in the Netherlands. Out of the three implemented models, the XGBoost model has the highest accuracy. XGBoost can explain 83% of the variance with an RMSE of €65,312, an MAE of €43,625, and an MAPE of 6.35% across the five municipalities. The two most important variables in the model are the total living area and taxation value, which were taken from publicly available datasets. Furthermore, a comparison is made between indexation and XGBoost, which shows that the XGBoost model is able to more accurately predict the appraisal values of different types of houses. The remaining unexplained variance is most probably caused by the lack of good indicators for the condition of the house. Overall, this paper highlights the benefits of open geospatial datasets to build a national real estate appraisal model. Full article

Urban quality, real estate values and property taxation are different factors that participate in defining how a city is governed. Real estate values are largely determined by the characteristics of urban environments in which properties are located and, thus, by quality of the location. Beginning with these considerations, this paper explores the theme of urban quality through a study of property values that seeks to define all physical (and thus measurable) characteristics that participate in defining urban quality. For this purpose, a multiple linear regression model was developed for reading the residential real estate market in the city of Pescara (Italy). In addition to the intrinsic characteristics of a property (floor area, period of construction/renovation, level, building typology and presence of a garage), input also included extrinsic data represented by the Urban Quality Index. Scientific literature on this theme tells us that many independent variables influence real estate prices, although all are linked to a set of intrinsic characteristics (property-specific) and to a set of extrinsic characteristics (specific to the urban context in which the property is located) and, thus, to the quality of urban environments. The index developed was produced by the analytical and simultaneous reading of four macrosystems with the greatest impact on urban quality: environment, infrastructure, settlement and services (each with its own subsystems). The results obtained made it possible to redefine proportional ratios between various parts of the city of Pescara, based on a specific Urban Quality Index, and to recalculate market property values used to calculate taxes in an attempt to resolve the inequality that persists in this field. Full article

(1) Background—The aim of this paper was to indicate whether the taxation of facilities related to renewable or low-emission energy differed significantly from that of facilities generating electricity from coal. (2) Methods—The research was conducted using a descriptive method, and because of the legal nature of the article, a crucial role was played by the dogmatic method. (3) Results—The thesis according to which only the “construction part” is subject to the property tax is the result of many years of disputes between the taxpayers and the tax authorities. In practice, it is difficult to compare the tax burden on assets related to coal and low-emission power generation because of the construction of the tax base in Polish property tax law. Most often, however, the tax burden on assets, which is calculated in the context of the amount of energy produced, tends to favour coal-fired power generation. (4) Conclusion: The property tax regulations in Poland treat the assets used for energy production by all methods identically. In practice, because of the specificity of the tax base, this means a more favourable treatment of facilities associated with coal-fired power generation. Full article

The main bases for land taxation are its area or value. In many countries, especially in Eastern Europe, reforms of property taxation, including land taxation, are being carried out or planned, introducing property value as a tax base. Practice and research in this area indicate that such a change in the tax system leads to large changes in land use and reallocation. The taxation of land value requires construction of mass valuation system. Different methodological solutions can serve this purpose. However, mass land valuation requires a large amount of information on property transactions. Such data are not available in every case. The main objective of the paper is to evaluate the possibility of applying selected algorithms of machine learning and a multiple regression model in property mass valuation on small, underdeveloped markets, where a scarce number of transactions takes place or those transactions demonstrate little volatility in terms of real property attributes. A hypothesis is verified according to which machine learning methods result in more accurate appraisals than multiple regression models do, considering the size of training datasets. Three types of models were employed in the study: a multiple regression model, k nearest neighbor regression algorithm and XGBoost regression algorithm. Training sets were drawn from a larger dataset 1000 times in order to draw conclusions for averaged results. Thanks to the application of KNN and XGBoost algorithms, it was possible to obtain models much more resistant to a low number of observations, a substantial number of explanatory variables in relation to the number of observations, a low property attributes variability in the training datasets as well as collinearity of explanatory variables. This study showed that algorithms designed for large datasets can provide accurate results in the presence of a limited amount of data. This is a significant observation given that small or underdeveloped real estate markets are not uncommon. Full article

There are two different forms of property tax systems: value-based tax, which is used in most countries of the world, and area-based tax, which is used mainly in Central and Eastern Europe and developing countries in Africa. Area-based property tax provides more stable and predictable budget revenues. It is simpler to administer and scores worse on equity grounds from the perspective of the ability-to-pay principle of taxation. Against this background, Israel’s property tax system, known as Arnona, is complex, spatially diversified, and causes a lack of uniformity that leads to tax distortion. This paper’s primary purpose is to identify the weaknesses of Israeli property tax from 1997 to 2017 and indicate how to improve the property tax system. This paper is based on case studies from four of the most important cities in Israel: Tel Aviv, Jerusalem, Haifa, and Beersheba, which have four different measurement methods for calculating property tax. Unique data were collected from the Israel Central Bureau of Statistics. According to this analysis, it was found that there are substantial differences in property tax between the four cities over the two decades analyzed. The main weakness is the lack of uniformity of the taxation system; the solution is to unify the measurement of real estate area for tax purposes using drone technology. Full article

This study investigates the effect that property taxation has on investment in the real estate market. There is a close relationship between investments in the real estate market and taxes, local communities, public policies and economic development. The analysis was developed with reference to the Italian real estate market and its tax regime. In Italy, taxation on real estate affects possession, transfers and income. These three tax rates vary according to the subjects who exchange assets and manage them, to the intended use of the real estate property and to the options for choosing the type of tax regime permitted by law. On the basis of these parameters, a financial analysis of real estate investment is constructed and simulated in order to understand to which types of taxation investment is most sensitive. The results showed that a change in income taxation can have an important effect on the investment choice. This evidence may also suggest fiscal policy actions aimed at stimulating the real estate market. Full article

The traditional linear regression model of mass appraisal is increasingly unable to satisfy the standard of mass appraisal with large data volumes, complex housing characteristics and high accuracy requirements. Therefore, it is essential to utilize the inherent spatial-temporal characteristics of properties to build a more effective and accurate model. In this research, we take Beijing’s core area, a typical urban center, as the study area of modeling for the first time. Thousands of real transaction data sets with a time span of 2014, 2016 and 2018 are conducted at the community level (community annual average price). Three different models, including multiple regression analysis (MRA) with ordinary least squares (OLS), geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), are adopted for comparative analysis. The result indicates that the GTWR model, with an adjusted R² of 0.8192, performs better in the mass appraisal modeling of real estate. The comparison of different models provides a useful benchmark for policy makers regarding the mass appraisal process of urban centers. The finding also highlights the spatial characteristics of price-related parameters in high-density residential areas, providing an efficient evaluation approach for planning, land management, taxation, insurance, finance and other related fields. Full article

According to Italian legislation for a particular type of real property—lands/areas subject to buildability, but not yet currently buildable—there is a problem related to their “qualification”, or whether or not they must be considered buildable for the purposes of their recurrent taxation. These potentially buildable (POBU) areas, that were previously zoned as “agricultural”, have been rezoned as “general urban planning instruments/regulations” (the General Urban Development Plans or variances, which regulate land governance), whose approval path has yet to be concluded. Their value—the taxable base underpinning their taxation—clearly depends on their qualification (whether or not they are considered buildable). This has produced, in recent years, several disputes between owners and local governments; the law did not give univocal solutions: Today (2019), there is a conflict of case law in relation to considering these areas as being building areas, as it is not clear what estimating procedures should be used. This article is thus based on the assumption that responding to the problems connected with taxing POBU areas must be considered separately from (overcoming, in this way, conflicting case law) the “virtual” qualification of agricultural or buildable area, but must instead, and more simply, be considered as the actual condition it is found in (likelihood of having building potential in the future), and therefore its limitations (present at the time of taxation) and the time necessary for the building to actually be built and not just “potential”. The approach proposed in this article thus offers a solution to the problem that has been raised, by modifying the current de jure approach (defining the moment when the building right is manifested) towards an assessment/appraisal approach (defining the value of the potentially buildable (POBU) area, in relation to its actual conditions). To implement this approach, a methodology—proposing an upgrade of the traditional analytic procedure for the assessment of transformation value has been structured in a way such that consideration may be made of the components characterizing the potentially buildable areas by means of appropriate assessment parameters that go towards forming these areas’ value: These are the market value discount rate of the POBU area in relation to the uncertainty and risk of reaching effective and concrete buildability, and the estimated time needed to complete the procedural path for making the area actually buildable. Full article

A recent study of property valuation literature indicated that the vast majority of researchers and academics in the field of real estate are focusing on Mass Appraisals rather than on the further development of the existing valuation methods. Researchers are using a variety of mathematical models used within the field of Machine Learning, which are applied to real estate valuations with high accuracy. On the other hand, it appears that professional valuers do not use these sophisticated models during daily practice, rather they operate using the traditional five methods. The Department of Lands and Surveys in Cyprus recently published the property values (General Valuation) for taxation purposes which were calculated by applying a hybrid model based on the Cost approach with the use of regression analysis in order to quantify the specific parameters of each property. In this paper, the authors propose a number of algorithms based on Artificial Intelligence and Machine Learning approaches that improve the accuracy of these results significantly. The aim of this work is to investigate the capabilities of such models and how they can be used for the mass appraisal of properties, to highlight the importance of sensitivity analysis in such models and also to increase the transparency so that automated valuation models (AVM) can be used for the day-to-day work of the valuer. Full article

To promote rural revitalisation, China’s central government revised the land administration law to allow rural collective construction land (RCL) to be traded in the market and attract private and financial capitals into rural investment and development. However, the land value appreciation income of the market access is closely related to geographical location. Hence, the value appreciation of RCL is enormous in villages around cities and towns. By contrast, the land value appreciation of RCL is low in villages away from cities and towns. This marked difference will lead to a significant impact on the rural social structure. To avoid the excessive widening of the income gap in rural areas, China’s central government attempted to conduct land value capture by revising and implementing land tax laws and reasonably distributing the value appreciation income of market access amongst the state, collectives and individuals. In response to the requirements of land reform, this study firstly identifies the legal constraints on the taxation of RCL in China through the structured retrieval and organisation of legal documents on land taxation. Thereafter, the technical constraints are analysed through the structural retrieval and organisation of the technical specifications of China’s land valuation. Lastly, this study proposes a land administration domain model (LADM) valuation and taxation information model on the basis of the aforementioned constraints. The major contents of the proposed model encompass improving the information management of taxpayer identity registration, supplementing land valuation methods and strengthening valuation information of the large-scale influencing factors. The proposed model is the technical basis to prompt the interconnection between the real estate registration and real estate taxation systems, which will be conducive to the efficient collaboration of the two systems. Full article