A Capability Approach to Real Estate Valuation and Value Enhancement

There are numerous financial metrics available in the academic and commercial world to estimate real estate value. Appraisers often use such metrics when advising on the purchase or sale of real estate at a point in time. The first part of this paper proposes a new metric, based on the capability approach, to make an ex-post single period valuation. Further, appraisers often give advice to their clients on actions to take in order to enhance the value of their real estate. This area of value enhancement has received scant attention in the academic literature. In practice, this advice is often based ad-hoc, anecdotal recommendations. The second part of the paper develops seven real estate strengths that can be targeted and provides an ex-ante approach to building real estate value. The valuation model presented in this paper is a pragmatic approach to enhancing both the values of tangible and intangible capabilities of a property by utilizing Expense Leveraged Value Indexes (ELVI).

Analysis on Effect of COVID -19 on Property Valuation in Real Estate Market

The valuation of real estate is a central tenet for all businesses. Land and property are factors of production and, as with any other asset, the value of the land flows from the use to which it is put, and that in turn is dependent upon the demand (and supply) for the product that is produced. Valuation, in its simplest form, is the determination of the amount for which the property will transact on a particular date. However, there is a wide range of purposes for which valuations are required. These range from valuations for purchase and sale, transfer, tax assessment, expropriation, inheritance or estate settlement, investment and financing. The objective of the paper is to provide a brief overview of the methods used in real estate valuation. Valuation methods can be grouped as traditional and advanced. The traditional methods are regression models, etc. MRA has been implemented by many researchers to study valuation of real property cite that MRA is possible for coefficient estimates and factor weightings using a large number of actual sale cases. Keywords: Real property, property valuation, multiple regression analysis, SWOT Analysis

PRODUCING AND VISUALIZATING 3D BUILDING GEODATABASE AS A PART OF 3D CADASTRE PROJECT

Digital data production possibilities have developed with the emerging technologies, and it has become possible to use different data formats together. The usability of three-dimensional (3D) data on various application areas has increased with the multidimensional use of geographic data in established information systems, for 3D visualization, presentation, and analysis. Topography-related analyzes such as digital elevation models, digital terrain models, slope maps and visibility maps can be made from geographic data sets produced in 3D. In addition, the use of 3D data in Building Information Modeling (BIM) has added various innovations for geographic data analysis. In this study, a geographic database was established by taking the vector data produced in the 3D Cadastre project that was carried out by the General Directorate of Land Registry and Cadastre as an example. Data obtained from photogrammetry and architectural projects were used in accordance with the OGC CityGML standard. After creating 3D building database in GIS environment, as result of various visualization and analysis techniques, the contributions of this project to BIM were revealed for various applications such as real estate valuation, disaster management, renewable energy, 3D city models, and smart city projects.

USING SVR AND MRA METHODS FOR REAL ESTATE VALUATION IN THE SMART CITIES

Determination of real estate value plays a very critical role in economic development and basic needs of people. Increasing demand for real estate together with population growth is making it difficult to determine real estate value. In applications where real estate is the main subject, such as urban activities, smart cities and urbanization, urban information system and valuation systems, model-based value estimations are essential for effective land/real estate policy. The type of real estate and impact degree of features depending on the type should be known as well as value estimation. It will be beneficial to follow a method that both determines the real estate value and factor impact degree. With the studies to be carried out using such methods, both region-specific valuation models can be created and the model is established with the optimum variable. This paper aimed to determine real estate value by using Support Vector Regression (SVR) and Multi Regression Analysis (MRA) methods for effective real estate management. Besides, both methods were examined by revealing the impact degrees of features that affect the value. The methods were applied to 319 parcels in Konya. For each parcel, 31 land features and market values were collected. The parcel data collected since 2018 were included in the models. From the results, the RBF-SVR model reached the highest R2 value with 0.88, while the MRA model reached 0.86.

Determining the area corrections affecting the map areas in GIS applications

Nowadays, there are many area-based Geographic Information Systems (GIS) applications such as real estate valuation, land tax, farming support and cost–benefit analysis. Areas used in such applications are calculated by means of two-dimensional plane geometry. However, the computed area value is not the exact area value in the terrain. In order to calculate the exact area value of a parcel, area corrections due to various factors must be taken into account. These factors are selection of projection, slope of the terrain, elevation of the terrain and scale of the map. Selection of projection and slope of terrain are available; elevation of the terrain and scale of map are not available in all GIS software. In this study, the effect of area corrections on the area value calculated from the map is examined with sample applications and the results are presented to the GIS users. According to the results, GIS users should select the equal area projection. In addition, scale of map, elevation and slope of terrain should be taken into account in the area calculation where land measurements are not possible.

AN INTEGRATED APPROACH BASED ON ARTIFICIAL INTELLIGENCE USING ANFIS AND ANN FOR MULTIPLE CRITERIA REAL ESTATE PRICE PREDICTION

A relatively high level of precision is required in real estate valuation for investment purposes. Such estimates of value which is carried out by real estate professionals are relied upon by the end-users of such financial information having paid a certain fee for consultation hence leaving little room for errors. However, valuation reports are often criticised for their inability to be replicated by two or more valuers. Hence, stirring to a keen interest within the academic cycle leading to the need for exploring avenues to improve the price prediction ability of the professional valuer. This study, therefore, focuses on overcoming these challenges by introducing an integrated approach that combines ANFIS with ANN termed ANFIS-AN, thereby having a reiteration in terms of ANN application to fortify price predictability. Using 255 property data alongside 12 variables, the ANFIS-AN model was adopted and its outcome was compared with that of ANN. Finally, the results were subjected to 3 different error testing models using the same training and learning benchmarks. The proposed model’s RMSE gave 1.413169, while that of ANN showed 9.942206. Similarly, using MAPE, ANN recorded 0.256438 while ANFIS-AN had 0.208324. Hence, ANFIS-AN’s performance is laudable, thus a better tool over ANN.