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Integrating amenity scoring into real estate investment strategies represents an innovative approach. The case study focuses on the impact of amenities on residential rents, with Berlin serving as an example of a tight rental market and a dynamic city. The scoring and modeling approach offers a scalable framework for identifying high-potential investment opportunities.

The PATRIZIA Amenities Basic-Living-Need Scores© (BLNs) were developed to measure a location’s attractiveness based on the availability and quality of amenities. An AI-driven scoring algorithm takes the quality and quantity of all available amenities into account. The higher the scores, the better and more diversified is a location supplied with amenities for residential living. A generalized additive model is fitted to assess the strength and direction of impact of the BLNs on residential rents while controlling for other factors such as property and locational characteristics.

The results indicate that amenities are a fundamental determinant of rental values and tenant satisfaction, providing investors with actionable insights to maximize returns in competitive urban markets. Focusing on areas with high BLN scores can lead to better investment outcomes due to higher rental premiums associated with these locations. The study also highlights the need to run a detailed location analysis to avoid the pitfall of amenities congestion.

The results indicate that amenities are a fundamental determinant of rental values and tenant satisfaction, providing investors with actionable insights to maximize returns in competitive urban markets. By identifying and accounting for regional peculiarities, investors can make data-driven decisions that align with local tenant preferences and market conditions.

The PATRIZIA Amenities Basic-Living-Need Scores© are calculated via an AI-driven, scalable algorithm and were developed to measure a location’s attractiveness in relation to diverse areas of residential living. This research is pivotal as it empirically validates the utility of amenity scoring in optimizing real estate investment strategies.

The paper shows with two concrete examples about how algorithms are used in active real estate management. The paper also highlights that the discussion about the adoption of new technologies is crucial for market players.

The authors review the current status quo about new technologies in real estate and provide two examples of how algorithms can be used to understand locations and the value drivers of rents.

Location, location, location is nowadays data, data, data coupled with the knowledge of how to create life out of data. Algorithm can help to understand the value drivers of rents and can also help to evaluate the attractiveness of a location.

Real estate management will adapt to new technologies fast. This change has the potential to disrupt exiting strategies due to the increase in efficiency, insights, transparency and location knowledge. Investment managers walking this talk will definitely benefit in future.

The paper makes usage of the latest machine learning technologies applied to real estate investment cases. This is a unique opportunity on bringing light on the discussion about transparency in real estate.

This paper aims to provide an overview of the literature to point out similarities and differences among private ethical investors across countries and time. Over the past three decades, many surveys have been conducted to advance the understanding of the demographic characteristics, motivation and morals of private ethical investors across countries and time. To date, the survey-based evidence on private investors into ethical funds is geographically rather segmented, and the research questions are fairly diverse. This permits only very temporally or regionally selective conclusions. Thereby, the authors identify interesting topics for future research.

To identify the relevant literature for our review, the authors carried out a structured Boolean keyword search using major library services and databases.

When questions about negative screening criteria are presented in a direct investment context, the consensus of private ethical investors “worldwide” (on average) is that social screening issues are most important, followed by ecological and moral topics. The percentage of ethical funds in the fund portfolio of the average private ethical investor in Europe seems to increase when the investor exhibits high degrees of pro-social attitudes and perceived consumer effectiveness. European private ethical investors are of the opinion that ethical funds perform worse but are less risky than conventional funds.

The authors make suggestions on how investment companies should design their funds so that they can attract more socially responsible investors.

The paper is of particular value because it focuses on private investors in the fast growing retail market of socially responsible investment funds.