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This paper introduces insights into the Turkish real estate market, which can be generalized globally. It primarily aims to find the best forecasting algorithms for the housing price index and compare their prediction performance over three, six, nine, and twelve months ahead by using recurrent neural networks (RNN) with a comparison of out-of-sample predicting power of econometrical models. For these purposes, we employ three RNN architectures in twenty-four settings, revealing that certain RNN architectures are the best predictors in forecasting the Turkish real housing price index. The RNN architectures outperform traditional econometric models; however, the more months forecasted, the lower the prediction power. The lagged values of the price-to-rent ratio, real rents, and the lagged USDTRY values contribute more than the other predictors in forecasting the real housing price index. The outcomes suggest that stocks, real estate investment trusts, and gold are neither complementary nor competing financial instruments since housing is an illiquid asset.

Information on transaction prices and the ones characterizing the property as the subject of the valuation are essential for a proper valuation process. The accuracy and completeness of the collected set of information directly affects the quality of the valuation process. When market participants operate on the basis of unequal sets of information, information asymmetry is revealed. This research investigates the effects of information asymmetry on property market from the perspective of property registration abundance and mass appraisal systems. It explores how disparities in information abundance and quality within property registration and appraisal processes can affect market fairness and transparency. Employing a mixed-methods approach, it analyses property transaction data and tries to investigate effects of information asymmetry. The findings indicate that enhanced transparency and data quality can significantly reduce valuation discrepancies and lead to a more equitable real estate market. The study concludes with recommendations aimed at justifying information asymmetry’s negative effects, supporting for policies that promote information uniformity to improve the fairness and efficiency of property registration and mass appraisal practices.

Although many Chinese cities have made remarkable progress in promoting age-friendly housings, few empirical studies related to the premium price of housings with additional elevators installed have been reported. In this study, we constructed propensity score matching–hedonic price models as well as quantile–hedonic models to assess the impact of adding elevators on housing prices. This study concludes: (1) The impact of additional elevators on housing prices varies depending on the floor level. (2) Single-facing and older housings are more likely to be negatively affected by the addition of elevators, and this interaction increases with floor level. (3) Adding an elevator reduces the price of low-priced housings on low floors and raises the price of high-priced housings on low floors, while for middle-floor and high-floor housings, the premium increases with the price of the housing. (4) Compared with the commercial community housings, the elevator premium of the affordable community housings is higher. Our findings provide an important reference for local governments to formulate appropriate charging and compensation mechanisms for adding elevators to multistory housings to promote retrofitting of aging communities.

The university property management department has been facing a challenge with the strategic management of campus spaces due to the need for informal learning spaces (ILSs) to facilitate students’ independent studies. However, there’s limited research on how these ILSs perform, particularly concerning human-made sounds. This study delves into the impact of human-made sounds on students, considering their individual differences and positive experiences in various types of ILSs within an Australian university campus. The investigation includes open-ended questions to delve deeper into students’ positive experiences with human-made sounds across different ILSs. The research findings demonstrate that the impact of human-made sounds on students is influenced by certain individual characteristics. Additionally, the study identifies three types of positive experiences regarding human-made sounds in ILSs for students: a sense of relaxation induced by human-made sounds, the sound-masking effect, and increased motivation for learning. This study could assist university property managers in understanding students’ perceptions of human-made sounds and aid in strategic management of campus space that aligns with students’ needs and preferences for ILSs, improving the overall learning environment and support student success.

This paper explores the factors affecting the investment intention of individual real estate investors utilizing the extended theory of planned behavior. With the help of self-administered questionnaire, data from 366 individual investors from India was collected. This data was analysed using two-step structural equation modelling. While significant direct effect of attitude, external influence, financial self-efficacy and perceived financial return was found, interpersonal influence, perceived financial risk, facilitating conditions and financial awareness had no significant direct impact on investment intention. Upon checking the mediating effect of attitude on the factors, all factors influenced investment intention through attitude except facilitating condition and financial awareness. It was also observed that attitude stands out as the most important aspect due to strongest influence on intention directly and also providing mediation to all variables except two. The study guides policymakers and investment institutions to develop strategies and utilize resources in a direction that can bring out a positive outcome by strengthening real estate investors’ investment intentions. It brings out the fact that financial confidence should be boosted by enabling investors to handle and manage their finances which can bring in a positive attitude for investing.

The dynamics of housing prices can play a pivotal role in shaping household behavior. Motivated by the limited research on the impact of housing prices on consumption structure upgrading in China, this paper presents a series of analyses that deploy panel data from 276 Chinese cities between 2005 and 2019 to scrutinize this impact. Firstly, fixed effect models were used to investigate the relationship between urban housing price and the consumption structure upgrading, and it was found that urban housing price has an inverted U-shaped effect on the consumption structure upgrading of Chinese urban residents, while it has an inhibitory effect on the consumption structure upgrading of Chinese rural residents. Then, we conducted multiple robustness tests, all of which corroborated the benchmark results’ reliability. Finally, deeper than existing research, the mechanism of the impact of urban housing prices on the consumption structure upgrading was further explored and it was found that increasing urban housing prices can affect consumption structure upgrading by affecting residents’ marginal propensity to consume in development and enjoyment. Our findings provide key alternatives for China’s government to promote residents’ consumption structure upgrading.

Effective pricing is important for on-street parking management and proactive parking pricing is an innovative strategy to achieve optimal parking utilization. For proactive parking pricing, accurately predicting parking occupancy and deriving the price elasticity of parking demand are necessary. In recent years, there have been an increasing number of studies applying big data technology for parking-occupancy prediction. However, existing research has not incorporated economic knowledge into modeling, thus preventing application of the price elasticity of parking demand. In this study, proactive pricing strategies are proposed to adjust on-street parking prices which involve a parking-occupancy prediction model and a price-optimization method. Physics-informed neural networks are employed to achieve accurate prediction of parking occupancy and calculation of parking price elasticity. An elasticity-occupancy parking-management strategy is proposed for on-street parking management which leverages parking occupancy and price elasticity to guide pricing interventions. A case study shows that the parking-occupancy prediction model can make accurate predictions and derive the price elasticity of parking demand. Proactive parking pricing enables drivers to plan their trips in advance, allowing parking occupancy within an optimal range.

Rapid urbanization presents policymakers and planners with the challenge of balancing public open spaces design with the conservation and improvement of natural resources. A comprehensive understanding of the land economic value of urban blue-green spaces (UBGS) holds immense significance for urban sustainable development, urban spatial justice and the promotion of human well-being. In this study, the MGWR model is employed to discuss the heterogeneous effects of UBGS on housing prices in Hangzhou. Additionally, the interaction effect between blue space and green space was examined at the district level, and the specific locations and spatial patterns were identified. The results show that (1) different types, features and accessibility of UBGS have different degrees and spatial scale of effect on housing prices, and will be affected by other attributes of UBGS; (2) in 30.92% of the main urban area of Hangzhou, the effect of blue spaces and green spaces on housing prices exhibits an interactive effect. The spatial patterns are divided into blue-green positive synergistic, antagonistic and negative synergistic regions; (3) green space has positive and negative effects on housing prices, while blue space has positive effects on housing prices at the regional level. The existence of water bodies can promote the positive effect of green spaces on housing prices or alleviate the negative effect. The results indicate that planners must transcend the singular focus on blue or green space planning and instead consider both in an integrated manner. This outcome can provide valuable references for UBGS planning.

In the rapidly evolving real estate industry, integrating automated valuation models (AVMs) has become critical for improving property assessment accuracy and transparency. Although there is some research on the subject, no thorough qualitative systematic review has been done in this field. This paper aims to provide an up-to-date and systematic understanding of the strategic applications of AVMs across various real estate subsectors (i.e., real estate development, real estate investment, land administration, and taxation), shedding light on their broad contributions to value enhancement, decision-making, and market insights. The systematic review is based on 97 papers selected out of 652 search results with an application of the PRISMA-based method. The findings highlight the transformative role of AVMs approaches in streamlining valuation processes, enhancing market efficiency, and supporting data-driven decision-making in the real estate industry, along with developing an original conceptual framework. Key areas of future research, including data integration, ethical implications, and the development of hybrid AVMs approaches are identified to advance the field and address emerging challenges. Ultimately, stakeholders can create new avenues for real estate valuation efficiency, accuracy, and transparency by judiciously utilizing AVMs approaches, leading to more educated real estate investment decisions.

Enabling older adults to age at home is an urgent issue. This study focuses on the attitudes of middle-aged and older people (MAOP) in the capital cities of Taiwan, which are characterised by expensive housing prices and living costs, to examine their preferences for reverse mortgage (RM) schemes. The stated preference method and conditional multinomial logit model are utilised for analysis. The study simulates the total payment duration (TPD) and monthly payment amounts (MPA) to determine the market share of MAOPs’ choices regarding terms. The results indicate that MAOPs tend to opt for RM schemes when they have children, partly enhancing the preference toward the long-term alternative (AL). Increasing the MPA has a positive effect on the market share of the AL scheme, but the amount must be increased to 90% to replace the market share of non-participation schemes significantly. The experimental design of this study could serve as a reference for future RM scheme designs. The findings suggest that there should be more alternative funding sources in an ageing society, particularly through revitalising housing assets, to promote ageing in place.

In PPP projects, there is a tendency for investors to follow the government’s excessive behavior, which is not conducive to controlling the behavior risks of the project. Due to the differences in conformity purposes, it is important to determine the form and tendency of investor conformity to understand the reasons for conformity and propose control suggestions. Referring to the research of Song et al., the study constructs an initial scale that investor’s rational conformity to the government’s excessive behavior. 269 and 244 data are used for the pre-test and validation of the scale, respectively. After adjusting the scale and analyzing reliability and validity by using SPSS and AMOS, the forms of investor conformity are divided into abidance, compliance, and obedience. Meanwhile, the measurement scales of three conformity tendencies are determined. These findings are conducive to quantitatively evaluating the risk level of investors’ conformity and deepening managers’ understanding of rational investor conformity, which is beneficial for reducing the risk of investor conformity and thus reducing the risk of the government’s excessive behavior.

The agent-led construction system is a construction mode that emphasizes the introduction of professional management teams instead of government departments to develop public projects in China, including social security housing. In reality, the problem in practice is that the government owners’ management system of the agent market is not sound enough, and it cannot effectively motivate the agents. Existing research has not paid enough attention to this agent construction market. To reveal the effective incentives of agent developers, using the stimulus–organism–response theory, this study constructed a structural equation model and proposed a research hypothesis about the effect of project governance elements on project performance. The study found that governance elements including internal contractual governance, external contractual governance, and relational governance, had a positive incentivizing effect on the project performance. The psychology and behavior of the construction agent played a partial mediating role. The results provide a policy implication for city government to improve the incentive system for agent construction of security housing projects.

Public housing (PH) focuses on assisting low-income households with housing stability. However, the inefficiency of property management in PH gives rise to a range of challenges in both physical and internal operational management, leading to negative stigmatisation towards tenants living in PH. Various initiatives have been launched to address the inefficiencies of property management. This study categorised and elaborated these initiatives under three main categories: reconceptualisation, externalisation, and managerialisation. There is currently no systematic literature review that provides a comprehensive overview of the initiatives aimed at enhancing property management in PH on a global scale, despite the fact that an overwhelming number of initiatives have been proposed in various studies. Therefore, the present systematic literature review was conducted to provide a comprehensive understanding of the existing research and initiatives pertinent to PH property management. This thorough examination will not only inform the current state of property management practices but also provide valuable insights to guide future research endeavours and decision-making processes in this domain. The outcome of this study revealed the positive impact of managerialisation, showcasing its pivotal role in problem identification and as a strategic indicator for proposing solutions aimed at continuous improvement.

Due to High Voltage Transmission Lines (HVTL), easement expropriation is made and an easement price (compensation) is paid to the property owner. Since there is not a standard method, this price is calculated inappropriately and inaccurately. Therefore, either the property owner or the government suffers material damage. The main objective of this study is to determine the impact of HVTL factors on property value using the Analytical Hierarchy Process (AHP) method. Additionally, within the scope of the study, the aim is to develop a software model that automatically detects impairment rate and easement prices. In the study, a total of 79 individuals from 21 different countries participated in the national and international survey. The weights of the factors were determined using the AHP. A model software was created by integrating the weight values and the interface software of the model was created. The model software was tested with sixty expert reports prepared by different experts collected from ten different provinces. The rate of error in the expert reports was found to be at least 0.38% and at most 84.98%. With this software, an appraisal standard that will largely eliminate the existing injustices in the determination of the easement price will be provided. In addition, the inconsistencies between the expert reports of similar parcels and easements will be reduced, and easement prices will be paid fairly.

The housing price affects the allocation of family budget on consumption. We use regression analysis to confirm whether Taiwan’s housing prices have a significant influence on household consumption based on the questionnaire survey on household income and expenditure in 2018. We find that housing prices have the greatest impact on household consumption, possibly because housing accounts for the largest proportion of household assets, implying a significant wealth effect. In addition, the impact of interest of deposit on consumption is also found to be greater than that of household income as it is related to the level of total assets. The housing prices for single-person household with female gender have a negative and significant impact on household consumption. Finally, housing prices are found to have a significantly negative effect on non-consumption and current transfer expenditure. The possible reason is that the households who hold the house believe that the appreciation of housing price can provide more protection for the family from suffering from possible economic risks in the future and thus crowd out non-consumption expenditures.

Participating in megaproject experimental tasks would significantly improve the laboratories’ industry influence and future competitiveness. Thus, this paper introduces the long-term reputation effects of the incentive model of an experimental resource-sharing platform for megaprojects, which could motivate them to consider future benefits and improve their current efforts. The aim is to incentivize laboratories’ resource-sharing behavior more effectively and to increase the amount of resources shared by these laboratories on the platform, thus guaranteeing the long-term sustainability of the platform. It constructs the incentive model by combining dual implicit and explicit incentive mechanisms. It analyses the incentive mechanism of a reputation effect on laboratories compared with the pure explicit mechanism so that the primary conditions for reputation incentives can be obtained to achieve Pareto improvement. Finally, the proposed method is validated in combination with data simulation. The results show that although dual implicit and explicit incentive mechanisms could reduce the information asymmetry between the two sides and increase the efforts of laboratories and the benefits of the platform, the platform should not blindly increase the intensity of these incentives and need to consider the influence of the subsidies of these laboratories’ upfront inputs, the degree of sharing and their informatization capabilities.

Numerous qualitative review studies have been conducted to enhance the understanding of current research status of green buildings (GBs). Green building assessment methods (GBAMs) are crucial to the development of GBs and relevant research works has received wide attention. However, there are very few reviews to quantitively explore these studies. Therefore, this paper aims to systematically review literatures on GBAMs, and visually analyzes them through CiteSpace and HistCite. The article identified the most influential journals, contributors, representative institutions and regions. The knowledge bases of this area focus on “triple bottom line”, “indicator”, “building design”, “life cycle costing”, “life cycle sustainability assessment”, “tropical climate”, “building information modelling” and “Chinese green building label”. Through citation analysis, “updating existing GBAMs by scheme comparison”, “updating existing GBAMs by GB case analysis”, “establishment of new GBAMs” and “incorporation of BIM in GBAMs” were found as the main research themes. “Design”, “LCA”, “model”, “energy”, “management”, “residential buildings” and “office buildings” are high-frequency keywords. Future research directions were finally proposed as “more investigation on the other types of GBs”, “incorporation of cost-related criteria”, “enhancing health-related indicators” and“integrating with BIM technology”. The results could provide a useful reference to industry practitioners and scholars interested in GBAMs.

Internet technology and data-driven innovations are changing the way people live and work, which could have an impact on real estate supply and demand. Therefore, this study focuses on the relationship between urban digital economy growth and urban house prices. First, the empirical model shows that the digital economy has an inverted U-shaped impact on urban housing prices. The mechanisms underlying this relationship were then examined. The results indicated that fixed asset investment, government expenditure, and the urban environment influence the relationship between digital economy growth and urban house prices. Finally, a heterogeneity analysis revealed that the digital economy does not affect house prices in Tier 1, New Tier 1, and Tier 2 Chinese cities, but increases house prices in Tier 3 cities. In Tier 4 and Tier 5 cities, this relationship was found to be inverted U-shaped. These findings offer valuable insights to policymakers in China in balancing the growth of the digital economy and the stability of house prices.

In the housing market, the prices sellers would be willing to accept to sell their properties exceed the prices buyers would be willing to pay for them. Behavioural economics postulates that this discrepancy is due to the endowment effect, which is a cognitive bias that causes the overvaluation of the goods we own. However, the existing literature lacks theoretical considerations and empirical evidence on the impact of the use of real estate agents by parties to transactions on the endowment effect in the housing market. Therefore, this study aims to assess the influence of real estate brokerage on the endowment effect using the example of the Polish residential market. To achieve the purpose of this study, a lab-in-the-field experiment was conducted with 248 respondents divided into sellers and buyers. The results indicate that the participation of real estate agents during transactions does not lead to the weakening or elimination of the endowment effect but, in some cases, to its intensification, which is due to the framing by buyers and sellers of the commission charged by agents as a loss requiring compensation. This research confirms the inefficiency of commission-based real estate brokerage services and points to possible corrective actions for legislators.

Land supply in China is planned by governments, and the supply ratios of various types of land are in relatively rigid administrative control. This paper constructs a DSGE model to study the relationship between land supply marketization in China and economic fluctuations. Moreover, this paper evaluates the social welfare gains from land marketization. We document that the government’s land planning constraint impacts consumption and social welfare through the land price and final output, causing resource misallocation losses in aggregate investment and output. Quantitatively, the optimal upper limit of industrial land supply is about 40% to achieve the maximum social welfare. This paper stresses the necessity of revitalizing China’s land market, and simultaneously speeding up the marketization of collectively operated construction land.

Shopping center plays an important role in distribution system and marketing. These canters provide an appropriate atmosphere for customers; so that, the customers achieve the best service within a short time. However, there is an intense competition among shopping centers to attract more customers for increasing the profit. Therefore, a powerful model assists authorities in identifying the critical competitive aspects and directing their efforts toward performance improvement. However, a number of strategies have been developed to identify the most relevant components. The Delphi technique under intuitionistic fuzzy environment, called intuitionistic fuzzy Delphi method (IFDM) study, is a group-based technique that can simply formulate the uncertainty imposed by decision making circle. On the other hand, multi criteria decision making (MCDM) method such as analytical network process (ANP) is a mathematical tool for taking into account mutual relationships in order to rank a number of criteria. Nonetheless, the ANP is unable to account for the uncertainty involved in the decision-making process. Similarly, the intuitionistic fuzzy set (IFS) can express ambiguity and vagueness by utilizing the given scale. Because the IFS is robust in dealing with complexity and ambiguity, the IFS-GANP (an integrated model of the IFS and ANP methods under group decision) can result in a more specific description of the situation. As a result, the IFS-GANP approach outperforms both conventional ANP and fuzzy ANP. To demonstrate the model’s feasibility, a case study rating the essential aspects impacting the attractiveness of retail centers is shown. The result demonstrates factor C31 (Location) with value of 0.202 plays the greatest role in attracting customers.

Performance-based payment mechanism is one of the key issues to ensure all stakeholders’ benefits in infrastructure Public-Private Partnership (PPP) projects. However, most existing research on performance-based payment with a fixed incentive coefficient can’t play a good incentive role. This study aims to the intrinsic mechanism between the performance appraisal score and the performance-based payment structure, so as to design the dynamic performance-based payment mechanism for infrastructure PPP projects. Firstly, the multi-objective optimal method is used to calculate the unit-payment. Second, principal-agent theory is used to construct the performance-based payment model with a changeable incentive coefficient. The findings of this study show that, the performance-based payment mechanism can effectively motivate participants to provide high-quality and efficient services, because their remuneration directly depends on their performance. When the outcome does not meet expectations, the amount paid can be adjusted accordingly, thus ensuring the maximum protection of public resources as well as the private sector’s profits. They serve a dual purpose, on one hand, they offer insights to rectify the shortcomings in the current unsatisfactory payment structure. On the other hand, the study provides a theoretical reference for the public sector to effectively incentivize the private sector in enhancing project performance.

Urban worn-out textures, due to their ancient and historically valuable physical structures, exhibit low adaptability to urban developments and advancements. Tehran, as a city with a rich historical background, also boasts considerable worn-out textures. Due to economic, geographical, and political conditions, a large population resides in these textures. In this regard, the present research aims to investigate the livability status of residents, identify key indicators, and design scenarios in the worn-out textures of Tehran city. This paper proposes a new approach to MicMac that promotes driving forces to scenarios. The results indicate that the livability of residents in worn-out textures of Tehran city, particularly in central regions, is in undesirable conditions. Furthermore, the analysis of the MicMac matrix showed that housing prices, residents’ income, investment, and an increase in economic activities, as well as the role of urban management, are driving forces that have a vital impact on the livability of residents in worn-out textures. These driving forces have depicted three scenarios for the livability of worn-out textures in Tehran city until the year 2032.

Although vacancies in the real estate market have always been of key interest to both private and public stakeholders, measuring the impact of vacancy rates on property prices is challenging. This study attempts to quantify the extent of the influence of vacancy rates on the price decline of commercial properties. The dataset used in this study was collected and collated by valuation experts. We attempt to alleviate the bias inherent in the price estimates provided by the surveyors. A Bayesian multilevel estimation model was employed, and the results revealed that while some experts do not show deviations from the standard tendency of a peer group, others do diverge from this norm. Based on these findings, a bias-controlling price decline rate was derived. As many real estate statistics are produced based on survey data compiled by experts, the approach adopted in this study is expected to be applied to various real estate practices to mitigate the inherent and inevitable surveyor bias.

The study aims to investigate the heterogeneity of the Yangtze River’s impact on housing prices, using the data of 12,325 residential transactions within 8 kilometers of the Yangtze River in Wuhan, based on submarkets divided according to geographical location and buyer groups. The kernel density plots reveal that properties near the Yangtze River have the highest price and the lowest density, while properties further away from the river exhibit the opposite trend. Then the Spatial Generalized Additive Model and the Spatial Quantile Generalized Additive Model show the following results, respectively: (1) The Yangtze River has an influence range of roughly 5 kilometers on adjacent dwellings, with an average impact of 0.035%. However, within the chosen geographical interval, the impact rises from 1.582% to 2.072%. (2) The Yangtze River has the greatest impact on middle-priced houses, followed by high-priced houses, and the least impact on low-priced houses. (3) The Spatial Generalized Additive Model and the Spatial Quantile Generalized Additive Model have been proven to be effective at capturing spatial and temporal impacts on data. In conclusion, this article advises that the government should pay more attention to non-central locations with limited natural resources.

This study examines the transformative impact of the REIT Investment Diversification and Empowerment Act (RIDEA) of 2007 on Real Estate Investment Trusts (REITs) and their partnerships with operating entities in the U.S. senior housing industry. We explore how REITs, functioning as both asset owners and managers, adapt their business strategies in response to the evolving business landscape. Employing a case study approach rooted in Zott and Amit’s (2010) conceptualization of business models, the study identifies and analyzes notable shifts in market participants’ recognition of value-enhancing approaches, encompassing not only traditional rental income but also the operational performance of property managers. The findings reveal an expanded risk and profit-sharing mechanism propelled by the newly implemented business framework based on RIDEA, fostering enhanced alignment of interests between REITs and operators compared to the traditional business framework. While this effect under the new model holds the potential for significant enhancements in operational efficiency for asset managers, it concurrently introduces complexities arising from heightened financial and market risks, as well as challenges related to workforce management. Our findings offer valuable insights to industry experts, including REITs, operators, investors, and policymakers, enriching their comprehension of the evolving business models within the senior housing sector.

The booming real estate sector has been regarded as the “gray rhino” risk emerging in China over the past decade. Yet, the house price volatility per se has not been thoroughly examined. Filling the gap in the literature, this paper explores the house price volatility and its determinants for 70 large and medium-sized cities in China, using an extensive monthly data set from 2005 to 2019. We find evidence of significant geographical disparities in both the GARCH effects and the best-fitted volatility specification. Significant GARCH effects are found in 57 cities, among which 40% of cities show a persistent volatility pattern. We also find that both the house price volatility pattern and the associated volatility value are affected significantly by education and healthcare amenities.

In public-private partnerships (PPPs), governments sometimes may not afford the guarantees in full as initially agreed upon due to limited fiscal budgets. Accordingly, government overcommitment can occur. The degree of government overcommitment fulfillment affects not only the incentives provided by governments but also investors’ willingness to cooperate and efforts. However, until now, this government overcommitment risk has been ignored during negotiations. Moreover, little attention has been paid to the impact of investors’ fairness preference. To this end, this paper constructs an incentive mechanism of government overcommitment based on investors’ fairness preference in the horizontal and vertical dimensions. The results show that the optimal strategies of both sides are usually affected by the degree of government overcommitment fulfillment, investors’ fairness preference and their willingness to cooperate. Contrary to intuition, the optimal incentive of governments and investors’ efforts do not always increase with the decreasing degree of government overcommitment fulfillment, and thus, fairness preferences should be considered. It is not always the case that a higher-level government overcommitment fulfillment is better for themselves. The findings contribute new insights to the incentive mechanism to innovatively work toward creating an effective contract to pay more attention to government overcommitment risk in advance in PPPs.

The Chinese central government has instituted a series of macro-economic tightening and easing policy measures on the real estate industry to stabilize the housing market in recent decades. However, our understanding of these policies and their impact on real estate firms is limited. This paper constructs an index of government policies on the real estate industry by collecting national housing policies in China from 2007 to 2019. We use this index to quantify the impact of government policies on real estate enterprises’ performance, based on data from 103 real estate enterprises listed on the Chinese A-share market. Two important conclusions are drawn from the panel data regression. First, tightened real estate policies significantly lower real estate enterprises’ profitability (ROA and ROE), while increase their liquidity risk. Second, the heterogeneity analysis shows that the tightened real estate policies yield downward impact more mildly on state-owned real estate enterprises than non-state-owned enterprises.

In contrast to much of the literature that focuses on the issue of spatial dependence in housing price research, this study addresses the spatial aggregation of housing transactions and analyzes the effects of transaction hotspots and short-term flipping hotspots on housing prices by using real housing transaction data in Taipei City, Taiwan. The empirical results show that after controlling for the effects of spatial dependence and individual housing attributes, the impact of transaction hotspot areas on housing prices is significantly negative, while the impact of flipping hotspot areas on housing prices is significantly positive. The results verify that the key to driving up housing prices lies in flipping activities. Furthermore, the results of the spatial quantile regression model show that low-priced residential properties are more sensitive to the spatial concentration of housing transactions and flipping transactions in the housing market. Our results have implications for the government’s policy intending to control hot trade volumes to cool skyrocketing housing prices in a booming housing market. It is suggested that the government should pay attention to restraining short-term flipping activities in the housing market rather than setting constrains on housing transactions.

Policymakers and the public express concern regarding the volatility of housing prices due to its potential to increase consumer costs and negatively impact housing affordability. Based on empirical study, it has been seen that the expansion of the real estate sector has a significant impact on the investment in fixed assets by firms. This influence is mostly attributed to the alteration of the transmission of monetary policy. Real estate investment is considered a feasible option because of the significant and rapid appreciation in property prices. The primary objective of this study is to examine the influence of monetary policy on the housing market in China. To conduct the current study, macroeconomic data from a total of 44 time periods, ranging from the fourth quarter of 2012 to the fourth quarter of 2022, was collected. The findings of our study indicate that in the context of China, an expansion in the money supply has a greater propensity to positively influence the borrowing activities of real estate suppliers and clients, as opposed to the supply of properties themselves. The housing market can be influenced by governmental actions such as adjustments to the money supply and interest rates. While scholars have extensively examined the subject matter, the housing market in China remains relatively under-researched in terms of its susceptibility to government macroeconomic policies. Moreover, the current study offers a comprehensive overview of the prevailing challenges encountered by the residential property market in China, emphasizing the significance of macroeconomic policies within this particular context.

The scholarly modeling of property tax has always posed a challenge, with two primary concerns to be addressed: first, maintaining sustainability in the collection, which is primarily a concern for local governments; and second, ensuring fair distribution, which is of greater concern for citizens. In today’s practices, assessment-based property tax increases unmatched expenses in bubble economies. There is a substitution problem in rapid falls, the tendency to not decrease the assessments gives way to black holes and opens the door to ghost cities. This paper proposes alternative approaches, aside from market/land value or last sold price, aimed at improving sustainability and fairness rates. The dataset examined is based on 93.7K records and 88 attributes for assessed value of properties within the City of Buffalo, the United States of America. Since the label (Total Value) is a numerical and continuous value, regression models are selected, where ensemble machine learning methods categorically work well with larger datasets, combined with weak learners, like decision trees. Stacked Ensemble led the least error for regression with 0.98 R2, followed by Gradient Boosting. Results show a 79% dominance of uncontrollable attributes, such as Land Value, Neighborhood, and Sale (last sold) Price, compared to controllable attributes, such as Total Living Area, Construction Grade, Second Story Area, and many others. This article suggests having a more balanced split between uncontrollable and controllable attributes would contribute to both sustainability and fair distribution.

This study delves into the property submarket in Kuala Lumpur and Selangor, Malaysia. The submarket is anticipated to be simple, uniform, and dense, making it highly influenced by neighbouring properties. However, traditional data-driven methods that overlook spatial contiguity disregard this density condition. To tackle this problem, the study investigates spatially constrained data-driven methods utilizing Principal Component Analysis (PCA) and cluster analysis. The findings reveal that spatially constrained methods outperform traditional methods by minimizing errors and enhancing model fit. Specifically, the two-step cluster method and k-means cluster method reduce errors by 6.96% and 7.22%, respectively, but at the cost of model fit by 11.23% and 13.94%. Conversely, the spatial k-means and spatial agglomerative hierarchical cluster methods reduce errors by 8.68% and 8.17%, respectively, while improving model fit by 7.1% and 6.35%. Hence, the study concludes that spatially constrained data-driven methods are more effective in differentiating commercial property submarkets than traditional methods.

The present research has evaluated the spatial distribution and functional radius of land uses in the central district of Tehran city using objective and subjective methods. The findings showed; Land uses have a directional pattern with random distribution and a cluster. The functional radius also indicates a lack of desirable access for residents to land uses. On the other hand, the lack of per capita confirms the results of the functional radius of the land uses. Because the district is facing a per capita shortage in all uses and functional scales. The results of subjective evaluations also showed that residents lack ease of access to land uses. Also, the optimal access to land uses from the residents’ perspectives has been contrary to the standards of urban development plans. What has been quite evident is that spatial injustice in the distribution of land uses and the presence of disparities in access to land uses have had a negative impact on the livability of the district. Our results underscore the importance of a comprehensive examination of livability based on the spatial justice of land uses. This necessitates simultaneous attention to distribution patterns, functional radii of land uses, and the involvement of residents’ perceptions of that space.

The paper analysed the impact of different noise sources on the residential market. This research used the hedonic method in OLS, SAR and SEM models based on the data set containing geocoded 16,247 apartments in Poznan. Strong evidence was found that noise is negatively linked with apartment prices. The apartment prices were the most significantly influenced by railway noise – an increase of 1 dB above 55 dB causes a 1.79% decrease in the value of an apartment. For other noise sources, aviation noise had the most significant impact with a 0.59% decrease in value per dB, tram noise with a 0.32% decrease and road noise with a 0.12% decrease. The influence of different noise levels on apartments is not constant and does not assume a linear relationship. For road noise, noise below 60 dB and tram noise below 65 dB were statistically insignificant. This may indicate that these noise levels are acceptable and are compensated by better access to public roads or urban transport.

Large-city housing costs are commonly attributed to excessive investment or financialization of the property market. The primary cause of housing issues is the imbalance between housing supply and demand. In the context of the urban housing market, the dynamic matching of housing supply and population type plays a crucial role in promoting equilibrium, maximizing the effectiveness of the market’s long-term mechanism, and preserving the coordination and stability of the market. To illustrate this point, the paper analyzes the impact of different attributes of population on housing demand from the perspective of dynamic matching between population attributes and housing types. Taking Zhengzhou City in Henan Province as an example, we consider the relationship between population attributes and housing types in the past, construct a theoretical model of population and housing type and anticipate the future supply scale of various housing kinds. Ultimately, our findings provide a theoretical justification for curbing excessive financialization and housing bubbles in large cities. Furthermore, we propose a practical method for urban housing to return to the essence of life from the perspective of matching housing supply and demand.

With continuing urbanisation, real estate (RE) has become increasingly important in crisis management. The field of crisis research is still fragmented, especially in the RE context and focuses on the retrospective analysis of single crisis events. This qualitative, futures studies–based paper aims to build a more holistic, foresight-driven understanding of how diverse crises affect RE. It identifies 128 possible future crises from a societal perspective and adopts a novel method in the field, the Futures Wheel, to collect the views of 179 practicing and academic experts via 58 multidisciplinary workshops to analyse the impacts of the identified crises. From this analysis emerged the Crisis Impact Framework for Real Estate, which includes 23 impact themes that synthesise the variety of direct and indirect crisis impacts on RE’s hard elements (e.g. the physical condition of materials) and soft elements (e.g. economic value changes). This study contributes an integrated and foresight-driven perspective to the existing research literature. The framework can assist RE market participants in preparing and assessing critical elements of RE before, during and after crisis impacts have manifested and can help spatial planners, investors and RE managers to reflect in a more multidimensional manner on their vulnerabilities and crisis preparedness.

This study adopts the hierarchical linear growth modeling approach to analyze the differences in the changes of repeat-sales house prices in Kaohsiung City from 2012 to 2020. The Level 1 time-varying factors include house age and the time of repeat-sales; the Level 2 factors include house attributes such as house area, house type, and house location. Based on the results of the null model, the estimated variance is 0.42816, with a 1% level of significance. This shows that significant differences exist in the mean repeat-sales prices between houses. The interclass correlation coefficient is 91.65%, showing that the interclass variation and intraclass variation of the mean repeat-sales prices are 91.65% and 8.35%, respectively. The estimation results of the non-randomly varying slope model indicate that the sales time and sales time squared significantly affect repeat-sales prices. The annual growth rate and quadratic growth of sales prices do not differ by house type (luxury condominiums and apartment buildings) but are affected by house area and house location. The effect of house age on repeat-sales prices is moderated by house area, house type, and house location.

Urbanization, national economic growth, and China’s changing population structure have elevated the importance of real estate assessment in various contexts, including mortgage financing, secondary housing market transactions, and real estate tax reform. To address this need, this study employs a time-spatial double-fixed spatial cross-section data model as a mass appraisal tool to analyze the transaction price data of 429 ordinary residential houses in Xixia District, Yinchuan, China on April 1, 2022. Specifically, this study analyzes 7 spatial cross-section data models, discerning their interconnections. It devises an assignment technique that merges distance and characteristic variable rank into a unified indicator. The results explore spatial lag effects in real estate transaction price generation and assess the descriptive capabilities of different spatial cross-section data models.

Public real estate is considered an integral part of national wealth. It generates a wide range of economic activities that contribute to creating and providing common public goods. This study examines the efficiency of public real estate management in Lithuania. Our findings show that public property use, disposal, and management strongly depend on the managerial approach. We apply data envelopment analysis (DEA) in addition to the income value, regression, and correlation methods. We find that there are certain biases present due to the exclusion of some state-owned properties from the public register. Furthermore, we identify the need for greater precision in the indicators used for financial reporting of state-owned assets. The excessive number of vacant asset units poses a challenge, as it requires substantial maintenance expenditures. Moreover, the appraisal of alternative use of these assets is lacking, thereby limiting the potential for maximizing public benefits. Our analysis reveals that the rental price per 1 m2 and the residual value of the leased premises are the most critical determinants influencing the management efficiency of publicly owned property. Furthermore, it is evident that the residential property is the only type of state-owned property managed efficiently within the Lithuanian public sector. These findings underscore the importance of formation of robust public real estate policies.

Low- and middle-income groups face difficulties in homeownership because of the urbanisation and rising living standards, in which they rely heavily on the government’s housing provision. Yet, affordable housing provided in Malaysia has been repeatedly criticised as it is not up to homebuyers’ expectations and is not supported to reach sustainable goals. Hence, this research aims to investigate the influence of the sociodemographic background of the homebuyers on their preferences for sustainable affordable housing in Malaysia. The questionnaire was designed and responses were obtained from 316 low- and middle-income homebuyers. Spearman rank correlation and ordinal regression were used to investigate the association between homebuyers’ sociodemographic characteristics and their preference for sustainable affordable housing. Spearman correlation analysis revealed that gender, generation, marital status and monthly income were significantly correlated with sustainability criteria. The ordinal regression model reflected that stronger preferences for sustainable affordable housing were shown among female homebuyers, Gen Y and Baby Boomer homebuyers and married ones. The research findings close the gap between users (homebuyers) and the providers (developer/government) in the provision of affordable housing. Developers and the local authorities can provide sustainable affordable housing solutions that suit the needs of different homebuyers.

Using the real options approach, we try to evaluate the luxury value inherent in high-end housing and estimate its premium returns based on the simulation of the model. The key finding of the paper is that the luxury premium from the value of the high-end housing can be identified by the real options model, which is rarely documented in the literature. In addition, the luxury value per unit size of high-end housing can be imputed through the model simulation. Based on the results, we find that the changes of the estimated value per unit size can explain the dynamic housing market behaviour in the recessions and expansions over the business cycle. The luxury value will even become negative during the recession period. In summary, the luxury premiums of high-end housing are higher than those of general housing, but not all highend housing has positive luxury premiums. If sellers and/or builders of high-end housing cannot meet the conditions that maximize the utility of high-end housing buyers, negative returns will accrue from selling high-end housing.

The existing literature contends that high housing prices are a negative factor inhibiting population mobility and urban innovation. This paper explores the interaction effect among population mobility, housing prices and urban innovation. Based on panel data from the Yangtze River Delta region from 2010 to 2019, we establish a simultaneous equation model (SEMs) to test the relationship between population mobility, housing prices, and urban innovation vitality. The empirical results show that (1) population mobility and housing prices are positively correlated; (2) urban innovation vitality has a significant and positive effect on housing prices; (3) population mobility and urban innovation vitality are also positively correlated.

Water is essential to human survival; however, many people, particularly those in poorer countries, lack access to reliable sources of clean and safe water. Despite decades of effort to address this issue, water sustainability remains elusive even today. Smart technologies have been widely adopted for water management, rendering smart water management a promising path to achieving water sustainability. Nevertheless, the efficacy of such smart water management in attaining sustainability remains unclear. This study aims to investigate the progress made in pursuing water sustainability with smart water management through a scientometric literature review. The Scopus database was employed to extract a pool of 460 candidate publications, which were then analyzed for content using bibliometric analysis and VOSviewer software. Nine clustered research themes were classified, corresponding to the nine targets of sustainable development goals (SDGs) for water. The findings show that engagement across these topics is highly unequal, providing substantial guidance on where emerging research efforts should be concentrated. The novelty of this study mainly lies in the SDG perspective that provides a significant but overlooked angle for observing progress in smart water management domain. The findings can enlighten the international water industry to create a more favorable environment for regions lagging in using smart technologies to facilitate overall water sustainability.

Due to the land readjustment project in Gwangjin District, Seoul, Korea, in the 1970s, Gwangjin was characterized as a single-family residential area for individual households. Over the following 40 years, the land-use situation changed dramatically, and ironically, there is now a too-fragmented residential area and very insufficient commercial areas. We employed a balanced panel data analysis. The data for this study were 24,177 parcel tax assessments and land assembly, split, zoning change, and property development activities over nine years from 2011 to 2019. We found that de facto land assembly would affect the tax assessment by delaying it a year more than it is when the development is approved, while formal land assembly did not. Development activity itself increased the assessment for that year only. Finally, formal land assembly in the commercial zone increased the assessment only for the following year, while property development in the residential zone increased the assessment for that year only. We recommend that the government provide land-assemblyfriendly policy incentives to allow for much larger property developments in both residential and commercial zones. The research on land and property development activities’ impact on tax assessment can provide a reasonable basis for the government’s tax assessment institution building.

Unlike most studies on urban innovation, which focus on the effects of land-use policies, this study investigates the effect of urban innovation (UI) on land finance dependence (LFD). In China, innovation-related policies are intended to create a shared business environment where new entrepreneurs can contribute to achieving sustainable economic growth. This study analyzes the spatiotemporal dynamics of LFD and UI from 2005 to 2019 using the spatial autocorrelation model and dynamic spatial Durbin model. It finds a positive spatial association between LFD and UI throughout the studied regions during the study period. It also reveals a significant inhibitory effect of UI on LFD and a negative impact on the LFD of neighbouring cities. Moreover, the inhibitory effects will increase over time. Lastly, this study shows varied impacts of UI on LFD in different regions, especially in eastern China. Important policy recommendations for high-quality development in China are provided.

One of the most important goals of urban management is to provide comfort and well-being for all citizens. According to the population trend of the world, a massive segment of the future population will be the elderly. Therefore, it is crucial to pay attention to the needs of this segment of society. In this regard, the concept of an age-friendly city that provides a favorable urban environment for the elderly has entered the world’s scientific literature. Because one of the essential land utilization that the elderly have a lot of connection with is health-therapeutic use, the current research has been carried out to evaluate the current health-treatment situation of Songhor city in terms of its usefulness for the elderly. In the second step, the most important drivers influencing the location of these uses have been identified from the perspective of related experts. This research is applied in terms of purpose and descriptive-analytical in terms of research method. The required data has been collected using library-documentary studies, field surveys, and the distribution of questionnaires from related experts. According to the study’s findings, more than 60% of treatment facilities in Songhor city are inaccessible to the elderly. This difficulty varies by therapeutic use; in some cases, it is less difficult than the average, while in others, it is more difficult. In addition, three factors–investment cost (V3), rivalry (V10), and population density (V8)–have been identified as the age-friendly city’s main drivers out of a total of 18 main variables. The study’s findings indicate that the global population is aging, and urban managers and officials should design urban environments following the requirements of the projected population by putting forth medium- and long-term plans.

Businesses are eager to adopt new technologies such as augmented reality, blockchain, extended reality, and artificial intelligence, among others, as they see beneficial applications for strategic empowerment. As one of the technological disruptions, metaverse has caught the attention of the larger business community and facility management (FM) sector. In a world where various virtual realities converge, the hyper-connected digital universe known as the metaverse has the potential to fundamentally alter how FM and business interactions are conducted in the future. More people are spending time conducting business online, increasing the metaverse’s potential. Businesses are also making significant investments in the development of metaverse techniques. This study reviews the current literature to comprehend how metaverse techniques have shaped the building blocks of FM by looking at 206 papers. There is currently a dearth of functional metaverse research. This article aims to provide a thorough analysis of the integration of metaverse techniques for facility management. The review reveals several opportunities for further research into the FM-metaverse ecosystem, including improving data interoperability, enhancing the precision of point cloud data for as-built models of existing facilities, and producing efficient virtual property database integration.

Even though urban renewal has attracted much attention in recent years, relatively few studies focus on the sustainability of urban renewal projects, which is surprising given the associated economic benefits. To fill this gap, we review 31 typical cases of urban renewal in China that have existed for over 10 years and utilize fuzzy set qualitative comparative analysis (fsQCA) to identify causal configurations that lead to a high level of stakeholder satisfaction. Our findings show that: (1) there are four antecedent configurations that lead to the sustainability of urban renewal of urban renewal (these are: ~ preservation of history and culture * differentiation * project reformation * ~ incremental innovation; ~ preservation of history and culture * differentiation * commercial publicity * incremental innovation; preservation of history and culture * commercial publicity * ~ project reformation * incremental innovation; and preservation of history and culture * differentiation * ~ commercial publicity * project reformation * incremental innovation); (2) compared with other conditions, differentiation and incremental innovation play more important roles in the sustainability of urban renewal; and (3) not only the improvement of the environment but also the operation and management of projects should be considered carefully. This paper contributes to urban renewal literature and offers practical suggestions for sustainable urban development.

The performance-based payment structure has been widely used in infrastructure PPP projects. However, existing research has been absent on the structural unbalance problem of performance-based payment in the current infrastructure PPP projects. This study aims to construct an optimal proportion of the performance-based payment in the total payment, and then design a performance-based payment structure for infrastructure PPP projects. Firstly, the definition of the performance-linked rate is introduced to characterize the proportion of the performance-based payment. Secondly, based on the different objectives of the maximum social benefit and the minimum cost for the public and private sectors, a multi-objective optimization model is constructed to obtain a reasonable value range of the performance-linked rate. Thirdly, the impacts on different parameters in the performance-linked rate are revealed using simulation methods. Finally, the numerical and simulation results show that, for the weak social average ability, the social cost is also high, and a large performance-linked rate should be set. Conversely, when the social average ability is strong, the social average cost is relatively reduced, and a relatively low performance-linked rate should be set. Consequently, the results can guide the contract design in PPP projects.