Showing papers in "International Journal of Strategic Property Management in 2022"

Evaluation of infrastructure projects by a decision model based on rpr, mabac, and waspas methods with interval-valued intuitionistic fuzzy sets

Abstract: Infrastructure projects (IPs) face numerous challenges to reach the predefined aims over their life-cycle. There are many difficulties in projects because of the variety of elements in project’s tendency and the dependency of the project on mainly national factors. Due to these difficulties and their practices, the projects meet with uncertainty. In this paper, an interval-valued intuitionistic fuzzy set (IVIFS) is used at identifying ambiguity in IPs. Also, a new multi-criteria decision-making (MCDM) model is presented to evaluate and select the suitable alternative in IPs. Hence, a new IVIF-relative preference alternative-multi-attributive border approximation area comparison (IVIF-RPR-MABAC), and IVIF-weighted aggregated sum product assessment (IVIF-WASPAS) are proposed in order to obtain the weights of decision makers (DMs) and criteria, and a new IVIF-RPR-MABAC method is proposed to rank the alternatives. In this paper, a combination of the three mentioned approaches creates proposed new hybrid model to evaluate the main factors and the projects. Furthermore, a real case study is applied from the literature to validate the efficiency and performance of the proposed model. Afterward, a comparative analysis is presented to validate the proposed approach by comparing the hybrid proposed model with two IVIF-TOPSIS and IVIF-extended-VIKOR methods. The final results confirm the efficiency of the proposed model in ranking the main alternatives of an MCDM problem. Moreover, the sensitivity analysis is reported to determine the affection of parameters on the final weighting and ranking outcomes.

Incentive mechanism for performance-based payment of infrastructure ppp projects: coupling of reputation and ratchet effects

Abstract: The performance-based payment PPP model has been widely used in the infrastructure projects. However, the ratchet effect derived from performance-based reputation incentives has been largely overlooked. To overcome this shortcoming, ratchet effect is considered in the performance-based payment incentive process. A multi-period dynamic incentive mechanism is developed by coupling the reputation and ratchet effect. The main results show that: (1) Under the coupling of reputation and ratchet effects, the optimal incentive coefficient in the last performance assessment period is always greater than that of the first period. The bargaining power can replace part of the incentive effect; (2) Due to the ratchet effect, if the government improve performance targets through performance adjustment coefficients, it needs to increase incentives to overcome the decreasing effort of the private sector; (3) When the bargaining power and punishment coefficient are small, the reputation incentive is replacing the explicit incentive. The increasing incentive coefficient would make the ratchet effect dominant the reputation effect; (4) To prevent the incentive incompatibility derived from the ratchet effect, the government should increase the incentive while increasing the punishment to achieve the “penalties and rewards”. This study provides theoretical and methodological guidance to design incentive contracts for infrastructure PPP projects.

Farmers’ livelihood, risk expectations, and homestead withdrawal policy: evidence on jinjiang pilot of china

Abstract: Although exploring the homestead withdrawal (HW) mechanism can optimize the allocation of land resource elements, the livelihood sources of farmers will change and face different sources of risk. Many studies have explored various factors affecting the HW. However, studies simultaneously exploring the relationship among farmers’ internal livelihood capital and external risk prevention capabilities and HW including differences among various HW models are still limited. The present study constructed a simple theoretical framework and used the probit model to analyze the decision-making behavior of 367 farmers in the Jinjiang Pilot in Fujian Province of China. Specifically, this study explored the impact of farmers’ livelihoods including natural, financial, and human capitals and risk expectations. Such risk expectations involve living conditions, social security, residential environment, and psychological conditions on HW in asset replacement, index replacement, and monetary compensation model. The empirical findings indicated that the farmers’ livelihoods and risk expectations have inconsistent effects on farmers’ HW decision-making in all the models, except for risk expectations. In other words, social security and residential environment have a significant inhibitory effect. These results implied that differentiated policies for HW should be considered to enhance the farmer’s sustainable livelihood capacity and controllability of risk.

Exploring hotel appraisal determinants amid sales trend during covid-19 pandemic: using a danp-mv model

Abstract: In the current hotel sales trend due to COVID-19 pandemic, few empirical studies have discussed hotel appraisal determinants and prioritization in terms of operational efficiency. This paper presents an innovative approach for appraisal practice efficiency based on hotel appraisal approach and the multiple criteria decision making (MCDM). The DANPmV model is used to identify the determinants related to actual hotel appraisal practices, including the techniques of the decision-making trial and evaluation laboratory (DEMATEL), DEMATEL-based ANP (DANP), and modified VIKOR. The result of influential network relationship map (INRM) and the gaps of determinants to the aspiration level may contribute to improving hotel appraisal efficacy. In practice, the “discounted cash flow” becomes the most influential determinant (dimension) and the “market survey” is the most manageable one. More findings together with an action plan are presented and useful in the real world. Therefore, this innovative approach could help hotel appraisers and related parties, such as hospitality managers, investors, lenders, and decision makers, better manage the evaluation determinants of hotel appraisal efficacy.

A multicriteria evaluation system for large real estate investments

Abstract: As an economic engine of contemporary societies, the real estate market needs to be carefully analyzed in terms of both urban management and private or public investment. Information on this market’s behavior can facilitate the identification of turning points in societies’ economic history. Analysts should focus not only on conditioning variables and other important determinants of relevance to investment evaluations but also on the impacts of each variable or factor. Measuring these effects is a key activity in decision-making processes. Given real estate’s growing importance to sustainable economic growth, this study concentrates on creating a multicriteria model for evaluating large real estate investments. A constructivist, process-oriented approach was applied in group work sessions held with a panel of experts with experience in dealing with this issue. These specialists structured the problem of evaluating large real estate investments using value-focused thinking (VFT) and cognitive mapping. The best-worst method (BWM) was then applied to calculate trade-offs among decision criteria and calibrate the evaluation system. The results were presented to and validated by a representative of Instituto da Habitação e da Reabilitação Urbana (Institute for Housing and Urban Rehabilitation), who identified the advantages and limitations of the proposed model, and suggested possible improvements.

Ranking residential neighborhoods based on their sustainability: a cm-bwm approach

Abstract: Population growth and rapid urbanization have consequences that are reflected in the economic, environmental, and social stability of city-residential neighborhoods. These impacts directly affect not only residents but also real estate markets and local governments. The professionals working in the latter entities have become increasingly concerned about urban sustainability and its strategic integration into their plans. Strategies have been implemented that focus on both addressing negative aspects of residential neighborhoods and enhancing positive features that can contribute to the continuous improvement of locals’ living conditions. This study applies the multiple-criteria decision analysis approach and a combination of cognitive mapping and the best-worst method (BWM) to identify the most relevant criteria and use these to rank residential neighborhoods according to their sustainability. To apply the selected techniques, two group meetings were held with a panel of decision makers. The results were validated by the panel members and the Funchal City Council councilor for urbanism, who concurred that the proposed ranking system facilitates the identification of the most sustainable residential neighborhoods. The contributions and limitations of the methodological approach are also discussed.

Spatial effect of market sentiment on housing price: evidence from social media data in china

Abstract: Market sentiment has become more easily spread between cities through social media. This study investigates the spatial effect of market sentiment on housing price in a social media environment. In order to extract home-buyer sentiment from social media, we use text sentiment analysis techniques and build a novel housing market sentiment index. A spatial econometric model of housing price volatility is subsequently constructed and the housing market sentiment index is included as an independent variable in the model. Using panel data from 30 large and medium-sized cities in China for 20 quarters from 2016 to 2020, the spatial effect of market sentiment on housing price is empirically analyzed by calculating direct and indirect effects. The results show that market sentiment had a significant positive effect on housing prices in the local and neighboring cities over the research period. However, the impact of market sentiment on housing price was heterogeneous in terms of geographical region; the direct effect was stronger in the eastern region than in the central and western regions, and the indirect effect was significant only in the eastern region. These findings can provide references for government to formulate housing market regulation policies and measures based on market sentiment.

House age and housing prices: a viewpoint of the optimal time for land redevelopment

Abstract: The value of a property comprises the value of both the building and the land. Numerous studies have reported a nonlinear relationship between house age and housing prices, which may result from mispricing the value of the land. The paper establishes a theoretical model to evaluate the optimal time for land redevelopment and the land value after redevelopment according to the real options. Although the depreciation effect causes the value of buildings to decrease as house age increases, properties with a lower residual building value have a higher probability of being redeveloped. Thus, the depreciation effect of building value and the inverse depreciation effect of land value contribute to the nonlinear relationship between house age and housing prices. Data from Taipei City, Taiwan, are employed for empirical analysis. The results imply that where land is scarce, housing prices are unlikely to depreciate due to ageing buildings.

A meta-analysis of the public-private partnership literature reviews: exploring the identity of the field

Abstract: The growing literature in PPP has made the field multi-disciplinary, over-differentiated, and unconsolidated. Taking a meta-analysis lens, this study investigates an unexplored identity of the field. It consolidates 61 review articles in PPP, analyses them across numerous review categories, and provides implications and suggestions for future studies. The review categories include the purpose of study, methods used, dataset details, journal and author details, primary disciplinary focus, awareness of previous review studies, and evolution of the PPP review literature. The findings reveal that the literature progressed through four evolution phases: from initiation, formation, growth, to expansion. Future review works should involve more empirical studies and examine the practical relevance of the PPP research. Promising areas are PPP governance, complexity, post-transfer phases, sustainability-related issues, and real estate development through PPP. The PPP researchers in construction engineering and management, property management, public management, and transportation will benefit from understanding the field’s identity, how it is currently being formed, promising areas, and where the literature is evolving.

Real estate valuation models performance in price prediction

Abstract: Using a sample of 900 apartments from Cluj-Napoca, Romania, containing selling transactions for the second semester of 2019, and data for 33 locational, physical and neighbourhood-related attributes (socio-cultural, environmental, and urbanism related), our research objective is to test the performance in price prediction, and hence the utility, of the Artificial Neural Networking (ANN), as artificial intelligence model versus the Generalized Linear Model (GLM), as a regression model. By contributing to an ongoing debate, our empirical findings confirm the results of a predominant group of earlier studies, namely the superiority of ANN. Precisely, we found that ANN can better predict selling prices and provides stability of results. Additionally, we addressed the critiques related to the transparency of results, showing that ANN also has the ability to illustrate the significance of the different attributes of real estate, if appropriate statistical indicators are used. These findings can serve the different real estate valuation purposes, including that of the review of valuation reports.

An online reviews information fusion method and its application to public property service quality evaluation

Abstract: With the rapid development of computer networking technology, people pay more and more attention to the role of online reviews in management decision making. The existing methods of online reviews fusion are limited to rational decision-making behavior, which does not accord with the characteristics of evaluators’ behavior characteristics in the real environment. In order to solve the online reviews fusion problem based on bounded rational behavior which is closer to the reality of property service quality evaluation, the multi-index and multi-scale (MIMS) method is extended into the generalized form, the online reviews are quantified by using the adverb structure scaling method, and an online reviews fusion method based on the improved TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) model is proposed. The feasibility and effectiveness of the proposed method are verified by an example analysis of property service quality evaluation. The research results are as follows: the adverb structure scaling method is suitable for a large number of online reviews processing, the proposed method improves the efficiency of online reviews information fusion, and it is feasible and effective to evaluate property service quality based on the bounded rationality of evaluator’s behavior.

Analysis of collaborative innovation behavior of megaproject participants under the reward and punishment mechanism

Abstract: Megaprojects are characterized by significant environmental uncertainty and technical complexity, which bring great challenges to engineering construction. Cross-organizational collaborative innovation is an important way to solve these problems. As the main body that understands the difficulties of the construction site and uses innovative products, the participation of megaproject participants is not only conducive to increasing innovation efficiency but also conducive to the application and promotion of innovative achievements. The collaborative innovation behavior of the participants in megaprojects under the reward and punishment incentive mechanism was studied. A game model between different participants was built by combining evolutionary game theory with prospect theory. Then, the dynamic evolution process of the collaborative innovation strategy of participants was analyzed, and the main factors affecting the evolutionary stability strategy of collaborative innovation through numerical simulation were examined. The research results indicate that reward and punishment incentives of collaborative innovation can encourage participants to choose the evolutionary stability strategy of participating in collaborative innovation from both objective and subjective aspects. Factors, such as the cost of participating, the synergy coefficient, the proportion of collaborative revenue distribution, and risk preference, can influence participants’ willingness to engage in collaborative innovation to different degrees.

Property management enabled by artificial intelligence post covid-19: an exploratory review and future propositions

Abstract: The Covid-19 pandemic outbreak across the globe has disrupted human life and industry. The pandemic has affected every sector, with the real estate sector facing particular challenges. During the pandemic, property management became a crucial task and property managers were challenged to control risks and disruptions faced by their organizations. Recent innovative technologies, including artificial intelligence (AI), have supported many sectors through sudden disruptions; this study was performed to examine the role of AI in the real estate and property management (PM) sectors. For this purpose, a systematic literature review was conducted using structural topic modeling and bibliometric analysis. Using appropriate keywords, the researchers found 175 articles on AI and PM research from 1980 to 2021 in the SCOPUS database. A bibliometric analysis was performed to identify research trends. Structural topic modelling (STM) identified ten emerging thematic topics in AI and PM. A comprehensive framework is proposed, and future research directions discussed.

Forecasting spatially correlated targets: simultaneous prediction of housing market activity across multiple areas

Abstract: This study involved the development of an approach to forecast house prices and trading volumes across multiple areas simultaneously. Spatially correlated targets, such as house prices, can be predicted more accurately by leveraging the correlations across adjacent areas. A multi-output recurrent neural network, a deep learning algorithm specifically developed to analyze sequence data, was utilized to forecast the house prices and trading volumes in the four chosen study areas. The forecasting accuracy of future house prices in one of the four geographical areas clearly improved; this area was found to be a price-lagging area, and the forecasting accuracy of this area significantly increased by exploiting the information of a price-leading area. As for the prediction of trading volumes, the difference in performance between the multi-output recurrent neural network and conventional models was very small. The results of this study are expected to promote the use of deep learning to predict the housing market activity through a simultaneous forecasting framework.

Research on property service mode innovation in the context of transformation and upgrading

Abstract: Property service mode innovation is the basic means of property service enterprise management. Discussing the property service mode in terms of transformation and upgrading is of rich research significance, taking Wenzhou Sapphire Property Service Co. LTD. in China (hereafter referred to as “Sapphire Service”) as an example. First, based on the development of property service in China, this paper summarizes the 4E mode of traditional property service, ensuring cleaning, ensuring greening, ensuring maintenance and ensuring security, and analyzes the existing problems. Second, combined with the development needs of the property service industry, this paper proposes the 4R mode of modern property service, realizing quality requirements, realizing pleasure service, realizing social responsibility and realizing green health, and summarizes its basic characteristics. Finally, based on the above modes in the practice at Sapphire Service, some management implications are put forward for the industrial transformation and upgrading requirements.

The impacts of task complexity, overconfidence, confirmation bias, customer influence, and anchoring on variations in real estate valuations

Abstract: Real estate valuation relies on real estate appraisers’ accurate assessments, which reflects the need to improve the objectivity of the valuation process. From the perspective of behavioral economics, appraisers are prone to numerous behavioral conflicts that could result in variations in their valuations. This study investigates the impacts of task complexity, overconfidence, confirmation bias, client influence, and anchoring on variations in real estate valuations. An online questionnaire was administered to 272 members of the Taiwan Real Estate Appraisers Association LINE group. Structural equation modeling was employed for analysis. A total of 150 valid responses were collected, yielding a valid response rate of 55.15%. The empirical results revealed that cognitive bias and client influence have significant and positive impacts on anchoring. Task complexity, overconfidence, and customer influence have significant and positive impacts on valuation variation. In addition to being the first study to explore the relationship between task complexity and valuation variation, this study also explored unconventional issues, such as the relationships between overconfidence, confirmation bias, and valuation variation. The results highlight the salience and value of behavioral economics-based analyses in empirical research on real estate valuation.

Pass-through business entity choice and earnings management: evidence from uk real estate investment trust conversion

Abstract: This empirical study innovatively investigates how the choice of a pass-through business entity and corresponding regulatory regime influence firms’ earnings management (EM) behaviors by testing on the UK Real Estate Investment Trust (REIT) conversion. A substantial proportion of UK non-REIT publicly traded property companies (LPCs) have chosen to become REITs since the UK REITs were launched in 2007. We conduct a series of tests on a database containing UK LPCs and REITs from 2000 to 2019 and find that conversion into pass-through business entity regimes like REITs that enjoy more favorable tax treatment but face more restrictions leads to more accrual earnings management (AEM) activity, but less real earnings management (REM) activity.

Investment in infrastructure as a key to sustainable economic recovery: the role of the building industry

Abstract: The focus of this paper is the role of the building industry in post-COVID-19 economic recovery plans. Investment in infrastructure forms a major part of many countries’ strategies to engender economic growth and construction is the pivotal industry in enabling the implementation of the plans in a sustainable manner. This study looks at the effects of investment in infrastructure on the economy with reference to the role of the construction industry in delivering this investment. Basic issues are considered, particularly concerning how relevant investment can be measured and how the contribution of the construction sector can be realistically assessed. A review of data sources and empirically peer-reviewed papers is undertaken. Based on longitudinal time series data from national statistics agencies and international organizations, analysis is undertaken to discover the relationship between infrastructure investment and economic growth. The study focuses on the UK, but comparisons are made with other countries to consider alternative approaches to stimulus investment policies with digitalization, and sustainability and green investment being a growing feature of investment plans. Potential issues of these approaches are examined and the main barriers to their achievement are identified. Emerging trends and a set of policy agendas are proposed to guide future directions.

Quantitative view assessment (quvias) method for window visibility analysis utilizing bim, gis and web environments

Abstract: The developers of the construction project assess the economic feasibility of the project at the early stages of project development and analyse possible alternative solutions. This research focuses on the assessment of property attractiveness and building location problems at an early stage of project development and proposes the original method for visibility analysis based on the utilization of Building Information Modelling (BIM), Geographic Information System (GIS) and Web environments. The proposed Quantitative View Assessment (QUVIAS) method allows to assess the view mathematically and presents it as a quantitative parameter. The proposed method considers the mathematical shape of the view as a sphere and utilizes spherical coordinates that remove distortions and increase the accuracy of the analysis. The presented approach determines quantitative view coefficients for alternatives of windows, premises and buildings, including their comparison. The way of determining the view proposed in the QUVIAS method can help decision-makers to make more accurate decisions during the selection of a project development strategy. The experimental analysis proved the usefulness of the proposed QUVIAS method in the assessment of the rational building location and prediction of project revenues as well as potential usefulness in the estimation of property attractiveness.

Does machine learning prediction dampen the information asymmetry for non-local investors?

Abstract: In this study, we examine the prediction accuracy of machine learning methods to estimate commercial real estate transaction prices. Using machine learning methods, including Random Forest (RF), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Deep Neural Networks (DNN), we estimate the commercial real estate transaction price by comparing relative prediction accuracy. Data consist of 19,640 transaction-based office properties provided by Costar corresponding to the 2004–2017 period for 10 major U.S. CMSA (Consolidated Metropolitan Statistical Area). We conduct each machine learning method and compare the performance to identify a critical determinant model for each office market. Furthermore, we depict a partial dependence plot (PD) to verify the impact of research variables on predicted commercial office property value. In general, we expect that results from machine learning will provide a set of critical determinants to commercial office price with more predictive power overcoming the limitation of the traditional valuation model. The result for 10 CMSA will provide critical implications for the out-of-state investors to understand regional commercial real estate market.

Impact of access to sunlight on residential property values: an empirical analysis of the housing market in shanghai

Abstract: As an important environmental amenity, sunlight brings us a large number of benefits and improves the quality of our daily lives, and its welfare measurement depends on concrete living conditions. The purpose of this article is to empirically document the non-marketed value of sunlight in light of the view orientation of an apartment in the context of the housing market. Using a hedonic pricing model estimated with the real estate transaction data over 40,000 housing units in 2019–2021 in Shanghai, it is found that: (1) homeowners, on average, are willing to pay an extra 7.2% to choose the apartments with a high level of sunshine (facing south), relative to those with no direct access to sunlight (facing north); (2) the value of sunlight shrinks with pollution and becomes larger if living in a higher apartment; (3) residents living in higher units have a larger willingness to pay for the sunlight and environmental quality improvement. These empirical findings shed light on the welfare measurement of sunlight and have profound implications for the capitalization of environmental amenities reflected in housing prices.

Path analysis on escalation of commitment of investors in different project scenarios of ppps

Abstract: In public-private partnerships (PPPs), escalation of commitment (EOC) of investors often occurs when receiving negative feedback, leading to a great waste of resources and not conducive to the sustainable development of PPPs. The degree of project completion and decision-making responsibilities of investors with different conscientiousness and neuroticism may affect subsequent resource allocation and further influence their escalation behaviour. Thus, through scenario simulation, this paper constructs path analysis to illustrate the formation mechanism of EOC by investors with diverse conscientiousness and neuroticism in different degrees of decision-making responsibilities and project completion, in which confidence in completing the projects may present a mediating effect. The empirical results show that completion degree and decision-making responsibilities both positively affect investors’ EOC and that the interaction is significant. The impact mechanism of conscientiousness and neuroticism on EOC varies in different project scenarios. Then, some targeted recommendations are proposed to curb EOC. The findings provide scientific evidence for governments to conduct effective governance of EOC in PPPs.

Land marketization, secondary land market, and effects on industrial land price

Abstract: Land marketization and its effects are widely documented across developing countries. Few studies, however, have investigated the link between industrial land price determination and industrial land market reform, based on the establishment of a pilot open trading platform for the secondary market. Moreover, no studies have specifically examined the link between primary and secondary industrial land markets. This study, therefore, investigates industrial land price determination using a quasi-natural experiment to interpret distortions in industrial land prices in China. Using industrial land sales data for 2006–2017 in the city of Haining, Zhejiang Province, China, this study compares industrial land value in the secondary market with land transfer prices in the primary market and identify undervaluation in the primary market. The results show that the growth rate of industrial land transfer prices increased every year after the open trading platform was established. Moreover, compared with nonpilot districts and counties, industrial land prices in the pilot city (Haining) increased by 11.14% from 2015 to 2017. The findings suggest that, based on the pilot program, further market reforms should be undertaken by establishing open trading platforms in a broader area.

The motivations of chinese individual foreign investors in the australian residential real estate market

Abstract: There is an increasing number of Chinese individual foreign investors (IFIs) in Australian residential real estate markets, but few studies specifically focus on Chinese IFIs and comprehensively analyse these investors’ motivations in the Australian real estate market. This paper examines the motivations of IFIs in the Australian residential real estate markets. A qualitative historical research approach was employed to examine the topic. By using semi-structured interviews of Chinese individual investors (consisting of Chinese temporary residents and Chinese nonresidents) and Australian agents in Australian residential real estate from 2014 to 2015, the paper finds four common motivations (good living environment, stable political environment, cost efficiency and profit returns) and three distinct motivations (education, immigration and bandwagon effects) of Chinese IFIs. It was found that cost efficiency, profit returns, education investment and immigration tend to be articulated differently between Chinese temporary residents and Chinese nonresidents, although these four motivations were expressed by both. Chinese nonresidents consider cost efficiency and profit returns as their major motivations. In contrast to Chinese nonresidents, education, and immigration are the most important motivations instead of some traditional motivations such as profit returns or cost efficiency for Chinese temporary residents.

World heritage designation and property: the impact of preservation on the price of historic assets

Abstract: The restrictions and requirements imposed by historic preservation regulations bring about many changes to the rights of property owners. They might impose additional costs, most notably by prohibiting the demolition of designated buildings and thus decreasing property-development opportunities. The objective of this study is to examine what happens following world heritage designations; specifically, if and how such designations impact the property values of historic buildings. Using the hedonic price method, we measure the value of the option to demolish and rebuild that is denied to owners of designated buildings. We also measure the value of preservation regulations, expressed in the prices of apartments in designated buildings. The study area is the “White City” of Tel Aviv, which UNESCO designated as a world heritage site. The findings suggest that the market price of a designated building is on average 12.5% lower than its theoretical value if the building was not subject to historic preservation regulations. Moreover, a premium of approximately 14% was found in the price of apartment units in designated buildings if the building underwent preservation. These findings could have a direct impact on public policies designed to promote the preservation of historical buildings in world heritage sites.

Incentive mechanism for performance-based payment of infrastructure ppp projects: coupling of reputation and ratchet effects

Abstract: The performance-based payment PPP model has been widely used in the infrastructure projects. However, the ratchet effect derived from performance-based reputation incentives has been largely overlooked. To overcome this shortcoming, ratchet effect is considered in the performance-based payment incentive process. A multi-period dynamic incentive mechanism is developed by coupling the reputation and ratchet effect. The main results show that: (1) Under the coupling of reputation and ratchet effects, the optimal incentive coefficient in the last performance assessment period is always greater than that of the first period. The bargaining power can replace part of the incentive effect; (2) Due to the ratchet effect, if the government improve performance targets through performance adjustment coefficients, it needs to increase incentives to overcome the decreasing effort of the private sector; (3) When the bargaining power and punishment coefficient are small, the reputation incentive is replacing the explicit incentive. The increasing incentive coefficient would make the ratchet effect dominant the reputation effect; (4) To prevent the incentive incompatibility derived from the ratchet effect, the government should increase the incentive while increasing the punishment to achieve the “penalties and rewards”. This study provides theoretical and methodological guidance to design incentive contracts for infrastructure PPP projects.

Impact of major events on housing prices in hangzhou based on regression discontinuity

Abstract: Based on the regression discontinuity method, housing transaction price data of Hangzhou at the community level were used to assess the impacts of a serious of major events on housing prices in Hangzhou. The results show that the 2008 financial crisis had the strongest negative impact on housing prices, with prices decreasing by about 20%. Winning the right to host the G20 summit did not significantly impact housing prices, while holding the summit caused an increase in prices (about 10%). The first implementation of the home purchase restriction (HPR) caused a decreasing discontinuity of housing prices. Cancellation of the HPR did not cause a discontinuity effect, but the trend changed from negative to positive, and resumption of the HPR for local residents yielded the largest drop in housing prices, resulting in a 10% downward cut-point. However, the impact time was relatively short and did not change the rapid rising trend of housing prices.

The impact of urban renewal on neighboring housing prices: an application of hierarchical linear modeling

Abstract: This study explored the impacts of urban renewal projects on neighboring housing prices. Hierarchical linear modeling (HLM) was employed to analyze urban renewal projects in Taipei City. The Level 1 independent variables pertained to a house itself (19,157 pieces of data), such as its structure and neighborhood attributes. The Level 2 variable pertained to an urban renewal project (23 cases of urban renewal), and the explanatory variable was the scale of each urban renewal project. The study examined whether differences exist between the impacts of various urban renewal projects on neighboring housing prices, and analyzed the extent to which the differences in neighboring housing prices are caused by the differences between urban renewal projects. The empirical results showed that the mean housing price varies significantly between each urban renewal project. In regard to the variance in the mean house price, 31.46% was caused by the differences between the urban renewal projects. The estimated coefficient of the grand floor area of urban renewal (FLAREA) had a positive value and attained a 1% level of significance. This indicates that the larger the scale of an urban renewal project, the larger its effects on neighboring housing prices. The empirical results of this study could better explain the impacts of the scale of an urban renewal project on the externalities of urban renewal.

Power pyramid, hidden debt, and china’s urban expansion

Abstract: Chinese urban spatial expansion leads to inefficient use of land resources. This study uses the data of 289 cities from 2007 to 2018 and uses panel data models to test the driving mechanism of urban expansion. The study derives three conclusions. First, there is a significant positive correlation between urban power hierarchy and urban spatial expansion. If the urban administrative power hierarchy is high, then the urban built-up area increases more. Second, there is a significant positive correlation between the scale of hidden debt and the expansion of urban space. The increase of the urban investment bonds’ scale will promote the expansion of urban built-up areas. Third, there is a significant positive correlation between the quantity of bond issuers and urban spatial expansion.

Combination of machine learning-based automatic valuation models for residential properties in south korea

Abstract: The applicability of machine learning (ML) techniques has recently been expanding to include automatic real estate valuation models. The main advantage of this technique is that it can better capture complexity in the value determination process. Therefore, the performance of these techniques is shown to be superior to conventional models. In this paper, the latest ML algorithms (i.e., support vector machine, random forest, XGBoost, LightGBM, and CatBoost algorithms) are examined as automatic valuation models, and several combination methods are proposed to improve the models’ predictive power. We applied ML models to approximately 57,000 records on apartment transactions, which were provided by South Korea’s Ministry of Land, Infrastructure, and Transport, that occurred in Seoul in 2018. The results are as follows. First, ML-based predictors (especially, the latest decision tree-based algorithms) are more performative than conventional models. Second, the prediction error from a model can be partially offset by another model’s error, which implies that an efficient averaging of the predictors improves their predictive accuracy. Third, the models’ relative performance may be relearned by the ML algorithms, which means that they can also be used to recommend which algorithm should be selected for making predictions.