Cluster analysis

Cluster Analysis - Cluster analysis is a statistical method for grouping properties, locations, or market participants based on similar characteristics. In the real estate sector, it is used to identify submarkets, segment target groups, find comparable properties for valuation, and develop investment strategies. Cluster analysis reveals hidden patterns in large datasets and is a key tool in modern real estate marketing and research.

Applications in the Real Estate Industry

• Location Analysis: Grouping neighborhoods based on criteria such as purchasing power, population trends, infrastructure, and price levels - to identify micro-locations worthy of investment
• Market comparison: Formation of comparison groups of similar properties (year of construction, size, location, amenities) for valuation using the comparative value method
• Target group analysis: Segmentation of potential buyers or tenants based on sociodemographic and behavioral characteristics - for targeted marketing
• Portfolio analysis: Grouping properties within a real estate portfolio according to risk-return profile to optimize the investment strategy
• Risk management: Banks and insurance companies use cluster analysis to identify regional cluster risks in real estate portfolios and to differentiate loan-to-value ratios based on location

Methodology

Cluster analysis groups properties or locations into clusters in such a way that similarity within a cluster is maximized and differences between clusters are maximized. Commonly used methods include hierarchical cluster analysis (agglomerative or divisive) and the K-means method. The selection of characteristics (variables) determines the quality of the results-typical variables in real estate analysis include purchase price per square meter, rent per square meter, vacancy rate, population growth, purchasing power index, and unemployment rate.

In hierarchical cluster analysis, all objects are initially treated as independent clusters and are gradually combined into larger groups (agglomerative) or, conversely, split from a single group (divisive). The result can be visualized in a dendrogram, which shows the complete similarity structure of the dataset. The K-means method requires a specified number of clusters and assigns each data point to the nearest cluster center-particularly efficient for large datasets with several thousand observations, such as those provided by real estate portals and appraisal committees.

Application in Real Estate Valuation

In the context of valuation, cluster analysis helps to systematically select comparable properties. Instead of searching for subjectively similar properties, real estate is divided into groups based on four to eight characteristic variables-and properties within the same cluster are considered comparable. Appraisal committees and experts are increasingly using this approach to standardize and expand the data basis for the comparative market analysis.

Banks use cluster analysis in automated valuation models (AVMs) to identify suitable comparable data from their transaction database for a given mortgage case. This enables a faster and more cost-effective initial valuation of standard properties before an appraiser is commissioned. In portfolio valuation-for example, in the context of securitizations or credit risk management-cluster methods allow for the efficient segmentation of thousands of individual properties based on location, property, and market risk.

Cluster Analysis of Nuremberg Neighborhoods - Typical Investment Profiles

Cluster	Example Neighborhoods	Purchase Price of Condominium (approx.)	Rental yield	Appreciation potential	Risk profile
Premium residential area	Erlenstegen, Rechenberg, Bleiweiß	€5,000-7,000/m²	2.5-3.5%	Moderate-high	Low
Established/Stable	Maxfeld, Mögeldorf, Thon	€3,500-5,000/m²	3.5-4.5%	Stable	Low-Medium
Up-and-coming	Eberhardshof, St. Leonhard, Gostenhof	€2,500-4,000/m²	4.0-5.5%	High	Medium
Peripheral/Basic	Langwasser, Eibach, parts of Lichtenreuth	€1,800-3,000/m²	5.0-7.0%	Low-Medium	Medium-High

Source: Our own assessment based on the Nuremberg Appraisal Committee, empirica, bulwiengesa - Estimated values for approx. 2024.

Practical Tip for Property Owners in Nuremberg and Franconia

We recommend that investors in the Nuremberg metropolitan region consult cluster analyses from major research firms (bulwiengesa, empirica, CBRE) when searching for locations, as these group Nuremberg’s neighborhoods by investment quality. The results typically reveal three to four clusters: Premium residential areas (Erlenstegen, Rechenberg, Bleiweiß), established neighborhoods with stable demand (Maxfeld, Mögeldorf, Thon), up-and-coming neighborhoods with appreciation potential (Eberhardshof, St. Leonhard), and basic locations offering higher returns but also higher risk (parts of Langwasser, Lichtenreuth).

If you extend the analysis to the entire metropolitan region, you’ll find that Fürth, Erlangen, and Schwabach each have their own distinct cluster structures: Erlangen, with its strong university and technology hub (FAU, Siemens Healthineers), exhibits a particularly stable demand cluster with tenants possessing above-average educational qualifications. Always combine cluster analyses with local market knowledge-an experienced Nuremberg real estate agent is familiar with nuances within the clusters that no statistic can fully capture.

Frequently Asked Questions

Can I perform a cluster analysis myself?

For simple comparisons, a manual grouping in a table based on self-selected criteria (e.g., year of construction, location, condition) is sufficient. For a scientifically sound analysis, you need statistical software (SPSS, R, Python) and a sufficient data set with at least 30-50 observations per target cluster. The choice of variables is crucial here: If you only consider the purchase price, you get a one-dimensional sorting, not a true cluster structure. It makes sense to combine price indicators with demographic and infrastructure characteristics. Many real estate portals and market research firms offer pre-processed cluster analyses and location ratings that are well-suited for practical use and can be utilized without conducting your own data analysis.

How reliable are the results of a cluster analysis?

The quality of the cluster analysis depends crucially on the data set and the selection of variables. With a solid data foundation, cluster analyses provide valuable patterns and group structures that would not be visible to the naked eye. However, they do not replace local market knowledge: phenomena such as individual upgraded streets within an otherwise simple location, new infrastructure projects, or specific demand trends are only captured by cluster analyses after a time lag. Furthermore, different clustering methods can yield different results even with identical data-interpreting the clusters always requires a minimum level of expertise and market understanding.

Which data sources are suitable for real estate cluster analysis?

The best results are achieved by analyses that combine multiple sources: Purchase price collections from appraisal committees (actual sales prices), microcensus data (population, household income, household structure), asking rents from real estate portals (Immoscout24, Immowelt), GfK purchasing power data at the street level, and municipal statistics on population trends and vacancy rates. The Nuremberg Appraisal Committee publishes key baseline data in its real estate market report that can serve as a starting point. In addition, we recommend the regional market reports from CBRE and JLL for the Nuremberg metropolitan region, as well as the Deutsche Bundesbank’s Price Atlas, which documents purchase price trends at the district level.

Cluster analyses are no substitute for local market knowledge, but they are an indispensable tool for structuring investment decisions-especially when choosing between multiple locations in the Nuremberg metropolitan region that appear similar at first glance but have significantly different risk-return profiles.

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