In addition to the creative process of the appraiser, i.e. the valuation itself, real estate appraisal also requires cumbersome and time-consuming tasks, whose manual processing is often perceived as a nuisance and which also pose risks to the quality of the valuation.
Digitization offers new opportunities for the appraisal profession, which on the one hand allow more efficient ways of working by streamlining the process and at the same time can provide a more reliable and objective basis for the valuation.
In particular, the weak information efficiency of the real estate market can be countered by digital tools that enable the use of Big Data analyses, for example. As essential elements of valuation methods for investment properties, the discount rate and future earnings are critical variables whose determination poses major challenges for appraisers. A digital approach with the aid of Big Data can open up new avenues in valuation and provide relief for the appraiser. Instead of relying on conventional, analogous approaches, the authors choose cause-based approaches to determine these relevant input variables.
The level of the discount rate, i.e. the mathematical return on a real estate investment as the return on the capital invested, is essentially attributable to so-called market factors such as the key interest rate, which permanently determine the development and trends of a market and are therefore referred to as determinants - they determine supply and demand. These market factors include, in particular, macroeconomic as well as sociodemographic factors.
Using regression analyses based on TSCS (Time Series Cross Section) methods, a linear equation, the so-called factor model, was developed to determine the discount rate based on these determinants. The discount rate for the valuation of a property can be determined in a comprehensible way. This makes it possible for the first time to quantify the relationship between the real estate market and the general economic situation at a location and to use it to improve the quality of data on the real estate market.
Besides the discount rate, one of the main input variables for the valuation of a property is the rental income. The rent is not only dependent on the object itself but also on the market that it is located in. The different circumstances and conditions that are present in a certain market can be described and modeled with different parameters. Also, the way that a potential tenant is valuing the property´s characteristics may be dependent on the market conditions. These effects must be considered when building a model for the rental potential of a specific property.
By describing the effect of neighbouring cities on a location as well as the interaction of the value impact of property and location characteristics a rental model was developed that gives deep insight into the composition of value driving factors in the real estate market.
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