This final issue of the year for the Journal of European Real Estate Research (JERER) contains a compilation of six research papers that underscores a crucial shift in our discipline: the imperative to move beyond traditional, qualitative assumptions towards advanced quantitative modeling to accurately capture the true dynamics of value, risk and pricing in modern real estate markets.
The manuscripts included in JERER 18(3) are united by a fundamental goal: to enhance the scientific rigor and precision of real estate decision-making – be it in valuation, lending, or investment strategy – by moving beyond traditional, qualitative methods toward advanced quantitative models. They acknowledge the complexity and data intensity of modern markets, which necessitates the integration of several fields, like a deep knowledge of macroeconomic factors, spatial analysis and behavioral economics, in order to achieve precise valuation and risk models.
The common threads running through these papers are mainly three. The first is to advance in valuation and risk modeling, especially a push to modernize the income approach to valuation and integrate sophisticated risk quantification into credit decisions, recognizing that traditional models (like DCF) often fail to account for complex, correlated risks. The second is identifying true price drivers by a deep dive into the determinants of residential prices, moving beyond simple property features to incorporate macro-financial variables (GDP and interest rates), spatial dynamics (clustering) and psychological factors (sustainability awareness). The third is optimizing institutional investment strategy with the examination of how large real estate funds manage their portfolios, specifically assessing the financial impact of geographic diversification and localized proximity bias on fund performance.
The first group (first and second papers) focuses on the foundational methods used by appraisers and financial institutions to set value and assess risk. The common concern is the imperfection and uncertainty of information in calculating future cash flows and determining appropriate risk premiums. Hoesli (2025) discusses how the discount rate and/or capitalization rate can be estimated more accurately, moving beyond outdated models like CAPM, advocating for the use of multi-factor models and micro-level transaction data and highlighting the potential of machine learning. Spezzati and Tedeschi (2025) delve into how lending decisions can be improved by explicitly accounting for macroeconomic and financial risks that traditional DCF models ignore and the relevance of the introduction of Monte Carlo simulations to model correlated macro-financial scenarios, creating a robust “credit decision map” for risk-return optimization. Both papers tackle the limitations of traditional valuation methods (DCF). They argue for methodologies (advanced econometrics and simulation) that better capture the volatility and correlation of risk factors, making the input (discount rate in Hoesli, 225) and the output (loan terms in Spezzati and Tedeschi, 2025) more defensible.
The second group covers papers 3 to 5 and has, as a common feature, the investigation of the specific variables – from a macro perspective down to a behavioral one – that exert a significant, measurable influence on real estate prices in different market segments. Mazáček and Panoš (2025) discuss which macro-financial variables (e.g. disposable income, interest rates and GDP) are the key drivers of new residential prices in Prague by using statistical learning (Lasso regularization) combined with Bayesian model averaging (BMA) to select the most relevant variables. Toussaint et al. (2025) focus on identifying the impact of spatial clustering on luxury housing prices, whether the “luxury premium” exists and if luxury is always a safe investment. In this case, the authors use an instrumental variable (IV) approach combined with geographically weighted regressions (GWR) to handle spatial heterogeneity. Lachenmayer et al. (2025) investigate how psychological factors, specifically environmental awareness, influence the purchase intention for sustainable housing. The analysis is done on a survey-based analysis using the theory of planned behavior (TPB) and hierarchical regression to test the moderating role of behavioral variables. All three papers employ advanced modeling techniques to quantify causality or influence or test moderating effects and segment the market by focus: general residential (Mazáček and Panoš, 2025), luxury (Toussaint et al., 2025) and sustainable (Lachenmayer et al., 2025) and demonstrate that price drivers are market-specific.
Finally, in Group 3, Orpiszewski et al. (2025) focus on the operational and geographical decisions of large-scale investors (real estate funds) and how these choices ultimately translate into financial returns. The main question is how geographic diversification and proximity bias (investing near headquarters or within a shared linguistic region) affect the financial performance of Swiss real estate funds. By using cross-sectional regressions and spatial similarity analyses on a novel dataset of fund portfolios, the paper defines specific spatial metrics (distance and linguistic proximity) at the building level to analyze the trade-off between risk reduction (diversification) and operational efficiency (proximity and/or local knowledge). The finding that returns are not significantly affected by diversification but by scale (number of buildings) and age challenges the conventional wisdom of portfolio theory in this highly localized asset class.
I trust you will enjoy reading this issue.
Paloma Taltavull de La Paz
