Comparable analysis in real estate is one of the fundamental comparison methods used to determine the market value of a property. Under this method, the sale or rental data of properties similar to the subject property in terms of location, size, type of use, structural characteristics, and legal status are examined. The objective is to identify the price range at which comparable properties are traded in the market and to accurately determine the position of the subject property within that range. However, not every property located in the same neighbourhood or on the same street can be considered directly comparable. Factors such as the age of the building, floor level, orientation, view, accessibility, usable area, parking facilities, development rights, and physical condition may directly affect the value of a property.
For commercial properties, factors such as storefront width, pedestrian traffic, and proximity to main roads are also important in the comparison process. Therefore, comparable selection is not based solely on listing prices. The date, relevance, source, reliability, and degree of comparability of the data are evaluated together. Where necessary, adjustments are made for differences between comparable properties in order to reach a more realistic estimate of market value.
Once the strengths and weaknesses of the selected comparable properties and the subject property have been identified, comparison adjustments are applied. For example, a commercial property located on a main street is not considered to have the same value as a similarly sized property on a side street. Likewise, features such as parking facilities, a terrace, wider frontage, or superior construction quality may create differences in value.
Değer Gayrimenkul Değerleme evaluates market conditions, realistically achievable prices, and the characteristics of the properties together as part of its comparable analysis. Selecting appropriate comparables contributes to a more realistic determination of market value, whereas irrelevant or outdated data may lead to inaccurate results.
