Effective comprehension of highly complex and spatially heterogenous property market requires its’ appropriate recognition. One of the most critical steps in property analyses and valuation procedures is the identification of the sub-markets as the fundamental comparable units. The biggest challenge in this case is to define the criteria of the basis indicating the similarity (homogeneity) of property markets area. The objective of the study was to propose methodology (called “HO-MAR”) that enables objective identification of the homogenous (similar/comparable) areas (zones) that could indicate location of similar groups of property transactions (representative properties) used either in individual valuations or AVMs. The authors propose utilization of automated solutions based on robust geo-estimation that enables high efficacy of property submarkets identification. Robust geo-estimation is guaranteed by merging semi-automated data mining methods (e.g. entropy theory, rough set theory, fuzzy logic) and geoprocessing activities (Gauss filter, geocoding and reverse geocoding, tessellation model with mutual spatial overlapping) concerning spatial relation database application.

对高度复杂和空间异质的房地产市场的有效理解需要对其进行适当的认识。房地产分析和估值程序中最关键的步骤之一是将子市场确定为基本的可比单位。在这种情况下，最大的挑战是定义表明房地产市场区域相似性（同质性）的基础标准。该研究的目的是提出一种方法（称为“HO-MAR”），该方法能够客观地识别同质（相似/可比）区域（区域），这些区域可以指示类似的财产交易组（代表性财产）的位置个人估值或 AVM。作者建议使用基于强大地理估计的自动化解决方案，以实现房地产子市场识别的高效性。结合空间关系数据库应用的半自动数据挖掘方法（例如熵理论、粗糙集理论、模糊逻辑）和地理处理活动（高斯滤波器、地理编码和反向地理编码、具有相互空间重叠的镶嵌模型），保证了稳健的地理估计.