Search Results (312)

Announcements of major transport infrastructure can reorganize land markets long before construction begins, as expectations are capitalized into prices and building decisions—with direct implications for sustainable territorial planning. This study examines the real estate response to the 2024 announcement of the Torquemada airport project in the Valparaíso Region, Chile. We assemble a high-resolution microterritorial panel at the block–semester–land-use level, integrating three Chilean administrative registers: the SII cadastre (over 100 million construction lines across 16 semestral snapshots, 2018–2025), the F2890 conveyance records (1.49 million geolocated transactions), and the daily Unidad de Fomento series. We estimate a multi-outcome spatial difference-in-differences design, complemented by an event study, land-use heterogeneity analysis, local indicators of spatial association, placebo tests, spatial-weight sensitivity analysis, and the heterogeneity-robust Callaway–Sant’Anna estimator. We find a robust increase in new-parcel construction in the zone of influence—identified by an annual event study against never-treated controls whose pre-announcement coefficients are small and trendless, in sharp contrast to the uniformly positive pre-trends of the expansion and aggregate-stock series—together with selective appreciation of non-residential uses and no detectable effect on housing value. The expansion and aggregate-stock components are not separately identified: their pre-announcement trends are strongly non-parallel, so the corresponding fixed-effects coefficients are read as design-conditional associations. The evidence supports an activation of the extensive margin (new-parcel building) rather than a recomposition away from densification. We read the evidence as the anticipatory footprint of the announcement rather than a point causal effect. Detecting this footprint before construction enables anticipatory value capture and sprawl-containment policy while the planning window remains open. Full article

Traditional approaches to urban real estate green value assessment rely heavily on single structured data sources. Such methods often provide limited interpretability and fail to capture multidimensional green attributes accurately. To address these limitations, this study constructs a multimodal assessment framework that integrates image, text, and spatial information. A housing price prediction model is developed based on a Multi-Layer Perceptron architecture. Results show that the proposed method is superior to traditional models (such as the Hedonic pricing model, Ridge regression, and eXtreme Gradient Boosting, as well as single-modality control models). The core evaluation metric, mean squared error, reaches 0.0505 ± 0.0021. SHapley Additive exPlanations analysis shows that the text modality provides the largest contribution to model prediction, accounting for 51.45% of the global contribution. However, this dominance reflects the model’s dependence on textual green signals rather than the establishment of causal relationships. The result may also be influenced by marketing language bias and symbolic sustainability signals. The image modality contributes 38.48%, while the spatial modality contributes 10.07%, indicating a complementary relationship among the three modalities. Green premium analysis confirms that the model achieves higher prediction accuracy for high-priced residences and effectively captures differences in green premium across housing price tiers. This study provides a new technical pathway for real estate green value assessment. Full article

The primary goal of this paper is to analyse the Italian real house price index (RHPI). Classical approaches typically assume either stationarity or unit-root nonstationarity. Instead, this study adopts a fractional integration framework, where the differencing parameter is allowed to take any real value, including fractional ones. Using updated quarterly OECD data for Italy covering the period 1970Q1–2024Q2, the paper investigates the persistence properties of real house prices under alternative specifications for the short-memory component, including white-noise, Bloomfield, and seasonal autoregressive disturbances. The contribution of the paper lies in combining fractional integration methods, growth-rate dynamics, and structural-break analysis within a unified framework focused on the Italian housing market. The empirical results indicate a high degree of persistence in the series, although the estimated persistence parameter is sensitive to the specification adopted for the disturbance term. In particular, the specification based on Bloomfield disturbances produces weaker evidence of long-memory behaviour than the alternative models considered. To complement the persistence analysis, we also investigate the presence of structural breaks from a macroeconomic and historical perspective. The results suggest the presence of six structural breaks that are broadly consistent with the main cycles of the Italian real estate market identified in the existing literature. Full article

The real estate market is a complex and dynamic sector that plays a key role in economic stability and wealth generation. In many regions, real estate assets represent around 80% of household wealth, while rising housing prices have turned access to housing into a major social and economic challenge. In this context, the availability of accurate and accessible information is essential for decision-making by buyers, investors, and public administrations. This study proposes the development of an advanced technological tool based on Artificial Intelligence and Machine Learning techniques to predict and analyze real estate market dynamics within a specific geographic area. Using the city of Madrid as a case study, the research presents a digital application capable of estimating the market value of a property by analyzing comparable recently sold properties and incorporating key housing characteristics. By entering an address and a set of property features, the system generates a precise and data-driven valuation. The results demonstrate that AI-based approaches can significantly improve the accuracy and accessibility of real estate valuation processes. The proposed methodology enables real-time price estimation, graphical comparisons, and dynamic market analysis. Furthermore, the framework is scalable and can be extended to other geographic areas where relevant data are available, providing valuable insights for both academic research and practical decision-making in the real estate sector. Full article

Due to the high population density and limited land availability in Bangkok, the capital of Thailand, land values have been increasing every year, posing challenges to sustainable housing development. Accurate land valuation is critical not only for investment decisions but also for promoting economic efficiency, social equity, and sustainable urban land use. Inaccurate analysis can lead to losses for real estate developers, project residents, and surrounding communities. However, this process requires extensive knowledge and experience. This research presents an approach for analyzing land values in Bangkok using Deep Learning techniques, which can help real estate developers assess appropriate land values more accurately and precisely. The study collected data on vacant land in Bangkok from an online feasibility study database and analyzed them using Deep Learning techniques. The results showed 30 determinants categorized into five groups. The study conducted 80 parameter adjustments with a ratio of 128:64:32 using a Quadratic Loss Function. The model was validated using k-fold cross-validation to ensure robustness and a Model Simulator operator to test sensitivity analysis. The Deep Learning model resulted in an R-square value of 0.917 and an RMSE of 2620 USD. The results of this research can be used as an effective decision-making tool for real estate developers, landowners, and brokers in determining appropriate buying or selling prices for land to support real estate sustainable development. Full article

This paper presents an optimisation model for scheduling in-house energy consumption to improve efficiency and sustainability. Focus is on the integration of advanced scheduling techniques to improve the overall performance of the house appliances and energy storage system. The proposed model applies constraint programming and satisfiability (CP-SAT) techniques to analyse complex schedules. A sensitivity analysis was conducted by perturbing key input parameters, including electricity price variations and demand profiles, while tracking output metrics such as total cost, load distribution, and computational performance. The model incorporates real-world constraints, including fluctuating electricity prices and renewable energy availability, to improve efficiency and reduce operational costs. The optimisation of the scheduling task was set for a 36 h time period with time resolutions of 15 min, equal to the electricity price time step. The proposed approach is evaluated through simulation using representative household consumption profiles and real day-ahead electricity prices data. The performance of the proposed CP-SAT model was evaluated, and the model’s response to the input parameter change has been analysed. The computational performance and cost outcomes of the proposed CP-SAT approach are comparable to those reported for established HEMS optimisation methods. Full article

Housing markets display geographical linkages that contravene conventional regression assumptions; yet, Central and Eastern European towns are markedly underrepresented in spatial econometric research. This study provides a systematic spatial econometric analysis of Zagreb’s housing market. It looks at both asking sale and rental prices throughout the city’s 17 administrative districts. There are five model specifications used in the analysis: Ordinary Least Squares (OLS), Spatial Lag of X (SLX), Spatial Autoregressive Model (SAR), Spatial Error Model (SEM), and Spatial Durbin Model (SDM). The findings demonstrate significant positive spatial autocorrelation in both markets: Global Moran’s I = 0.29 (p = 0.007) for sales and 0.42 (p < 0.001) for rents. LISA analysis finds important groups of high-priced homes in the center districts and lower-priced homes on the edges. Spatial models significantly surpass OLS: SLX exhibits AIC enhancements of 9.90 (sales) and 20.20 (rentals), but SAR and SEM yield no enhancements, suggesting that local spillover effects from adjacent characteristics prevail over global spatial diffusion or correlated shocks. The higher Moran’s I and AIC gains in rental markets show that there are different spatial processes for different types of tenure. These results address a significant empirical deficiency in post-socialist housing research, illustrate that neglecting spatial dependencies may lead to biased estimates and reduced model performance, and furnish methodologically sound evidence that spatial econometric techniques are essential for accurate modeling for precise urban housing analysis in intermediate-sample scenarios. Policy implications stress the need to use spatial approaches in choices about property value, forecasting, and urban planning. Full article

This study introduces a new two-parameter exponential (TPEX) model for modeling skewed phenomena and risk analysis, motivated by the need for flexible yet tractable models capturing asymmetric behavior in actuarial, financial, and reliability data. An extensive simulation study evaluated seven estimation procedures: maximum likelihood estimation (MLE), ordinary least squares (OrLS), weighted least squares (WLSQ), Cramér–von Mises (CVM), Anderson–Darling estimation (ADE), Kolmogorov estimation (KE), L-moments, and Bayesian estimation, comparing bias, efficiency, and stability across sample sizes and parameter settings. Four real-data applications were conducted: two comparing estimation methods on relief and survival datasets and two assessing competitive performance against exponential-type models. Key risk indicators (KRIs), including the Value at Risk (VaR), Tail Value at Risk (TVaR), Tail Variance (TV), Tail Mean–Variance (TMV), and expected loss (EL), were computed using UK motor non-comprehensive claims and US house price data, illustrating the model’s relevance for insurance reserving and market risk assessment. Full article

This paper develops and applies a techno-economic optimization framework for sizing photovoltaic (PV) and battery energy storage systems (BESSs) in grid-connected energy communities. An in-house developed modeling platform featuring custom MATLAB (R2025a) code implements a mixed-integer linear programming (MILP) model that minimizes differential net present value (NPV) over a 25-year lifetime, integrating capital expenditures, operating cash flows, and carbon taxation. The formulation captures temperature-dependent PV efficiency, battery round-trip efficiency, and time-varying electricity prices, and is validated on a real campus energy community with hourly demand, irradiance, and tariff data. Two design scenarios are examined: the optimal unconstrained case and a budget-constrained configuration (CAPEX ≤ 2.0 M€). Results show the unconstrained system installs 3.19 MW_p PV and 12.3 MWh storage, achieving 78.9% self-sufficiency and a 78.9% emissions reduction. The constrained case installs 0.99 MW_p and 1.68 MWh, achieves 32.0% self-sufficiency, and delivers a 4.46 M€ NPV with payback in 3.9 years. Under current costs and tariffs, PV-dominated configurations provide the highest value, with limited battery benefit except under generous budgets or higher carbon prices. A dedicated CAPEX sensitivity analysis explores PV and battery cost variability and its impact on optimal sizing and economic outcomes. The core methodological contribution is a master-planning formulation that solves design decision variables and optimal dispatch concurrently within a single MILP. The flexible platform enables future reassessment as technology, tariff, and policy landscapes evolve. Full article

The decarbonization of the residential sector is a critical component of the European Green Deal, particularly in transition economies like Poland. This study proposes a comprehensive techno-economic optimization of a deeply retrofitted single-family house aiming for net-zero energy building (NZEB) status. The research specifically focuses on the Polish coastal climate zone, characterized by distinct humidity, wind, and temperature profiles compared to inland regions, which significantly influence the efficiency of air-to-water heat pumps (ASHP). Based on a real-world energy audit, the study simulates the synergy between a deep thermal envelope upgrade and a hybrid system comprising an ASHP, photovoltaics (PV), and battery energy storage (BES). This paper presents a detailed economic analysis of such hybrid systems under the new Polish ‘net-billing’ prosumer mechanism. The study evaluates the impact of electricity tariff structures (flat-rate G11 vs. time-of-use G12w) on the investment’s profitability. By calculating key performance indicators—including the levelized cost of energy (LCOE), net present value (NPV), and self-sufficiency ratio (SSR)—the research assesses various system configurations. The initial evaluation indicates that while deep retrofitting significantly reduces heating demand, integrating battery storage plays a critical role in enhancing economic returns under the net-billing framework. The analysis demonstrates that the optimized hybrid system (9.0 kWp PV + 10 kWh BESS) achieves an average annual self-sufficiency ratio (SSR) of 49.8% and reduces the non-renewable primary energy (EP) indicator to 0.0 kWh/(m²·year). Economically, the investment yields a positive NPV of €3194, an IRR of 5.25%, and a LCOE of €0.184/kWh, which is 34% lower than projected grid prices. Furthermore, switching to a time-of-use tariff (G12w) generates an additional 11% (€139) in annual savings. These quantitative findings provide actionable guidelines for policymakers and investors, confirming the financial viability and environmental benefit (annual reduction of 6.12 MgCO₂) of NZEB standards in coastal areas. Full article

This study empirically examines the impact of the Areas subject to permission of transaction (ASPLT) implemented by Seoul City on local real estate markets. Focusing on the case of the Seoul International District (MICE project area), where the regulated area (the geo-graphical districts subject to ASPLT) was initially designated, lifted, and later re-imposed, the analysis employs a modified repeat sales Difference-in-Differences (DID) methodology to assess its policy effect on housing price stabilization. The results indicate that the regulated areas experienced more subdued transaction volumes and price increases compared to non-regulated areas, suggesting the policy was effective in curbing short-term speculative demand. Additionally, neighboring areas exhibited signs of spillover effects due to displaced investment interest. The findings highlight both the utility and limitations of localized real estate controls and offer empirical insights for future policy design. Full article

Peri-urbanization in rapidly growing cities of the Global South is increasingly driven not only by demographic growth but by escalating inner-city land and housing prices that push households and developers toward peripheral zones. Bangalore exemplifies this transition, where housing affordability pressures, speculative real estate investment, and weak land governance interact to transform agricultural landscapes into fragmented built-up clusters. Using satellite imagery (1991–2024), census data, and GIS-based land-use classification, this study quantifies peri-urban expansion across eight clusters in the Bangalore Metropolitan Region. The results show rapid built-up growth, agricultural land decline, and increasing spatial fragmentation, reflecting processes of extended urbanization beyond formal city boundaries. These transformations produce environmental stress, infrastructure deficits, and socio-spatial inequalities. The paper situates Bangalore within planetary urbanization debates and argues that peri-urban sustainability depends on land market regulation, spatial planning capacity, and data-driven governance. Full article

The 15-min city (15 MC) is an urban planning concept that organizes cities through proximity-based systems, enabling residents to access essential services within a 15-min walk or cycle. Although the health and environmental benefits of this model are well documented, its effects on the real estate market have received limited attention. This paper examines the impact of 15-min proximity-based urban planning models on land use patterns, property values, and sociospatial interactions in urban settings. It adopts a scoping review approach (structured mapping and synthesis of the available literature) and, using a transparent source selection process (PRISMA-ScR), compiles evidence on how functional accessibility, mixed uses, and proximity to green/public spaces affect prices and rents in residential and/or commercial real estate. The synthesis shows that proximity is often capitalized as a proximity premium, but it can exacerbate inequalities and displacement risks without accompanying regulatory mechanisms. Based on the findings, an operational spatial–economic framework is proposed that links (a) planning interventions, (b) functional accessibility, (c) behavioral adaptation, (d) market valuation reactions, and (e) governance/redistribution tools (e.g., land value capture, inclusionary zoning), as a diagnostic tool for assessing surplus value and displacement risk and as a basis for future GIS/hedonic testing. Full article

We propose two narratives to analyze monthly returns for the S&P 500 Index. The first narrative emphasizes variables that represent the macroeconomy: Fed Funds Effective Rate, Real M2, 10-Year T-Note minus 2-Year T-Note, Shiller Housing Index, industrial production, and 1-Year Expected Inflation. The second narrative focuses on microeconomic fundamentals that include earnings, CBOE Volatility, consumer sentiment, interest rates, global price of copper, and the Dollar Index. We perform a methodology of 348 rolling regressions for each narrative, each with a sample of 60 monthly observations, and estimate the significance of the independent variables considered. We conclude that the microeconomic narrative with its indicators tied to stock market activities correlates with monthly returns more closely than macro fundamentals do. The new insight from this paper is that it is beneficial to employ both narratives as complementary rather than as competitive. Full article

This study investigates the challenge of affordable housing in Riyadh, a city undergoing rapid transformation aligned with Saudi Arabia’s Vision 2030. It aims to bridge the structural gap in the housing market by developing a comprehensive analytical framework that measures housing suitability for emerging middle-income families, linking it to economic, spatial, and behavioral dimensions. The research employs a sequential mixed-methods design. The first phase involved a Multi-Criteria Decision Analysis (MCDA) of 106 residential neighborhoods, constructing a Housing Suitability Index (HSI) based on financing cost (≤SAR 880,000), quality of urban life, and geographical accessibility. The second phase utilized focus groups with 16 participants from real estate developers and new families to explore behavioral drivers and subjective trade-offs. Quantitative results identified “convenience clusters” primarily in the city’s southeastern and southwestern sectors, offering an optimal balance between price and accessibility. Qualitative analysis revealed a significant trust gap and a misalignment of priorities: new families are increasingly willing to sacrifice unit size for central location and construction quality, a preference that conflicts with developers’ strategies focused on luxury units or peripheral projects for higher margins. The study concludes that achieving the 70% homeownership target requires a hybrid policy model, combining supply-side stimuli (e.g., subsidized land) with demand-side management (e.g., progressive mortgages). It recommends integrating the HSI into urban planning to direct investment towards logistically connected areas, fostering sustainable communities. Full article