استكشف المجموعة الواسعة من العناوين المتاحة.

We use changes in the value of a firm's real estate assets as an exogenous change in a firm's financing capacity to examine (1) the relation between reporting quality and financing and investment conditional on this change, and (2) firms' reporting quality responses to the change in financing capacity. We find that financing and investment by firms with higher reporting quality is less affected by changes in real estate values than are financing and investment by firms with lower reporting quality. Further, firms increase reporting quality in response to decreases in financing capacity. Our findings contribute to the literature on reporting quality and investment, and on the determinants of reporting quality choices.

This paper examines the impact of real estate prices on firm capital structure decisions. For a typical U.S. listed company, a one-standard-deviation increase in predicted value of firm pledgeable collateral translates into a 3 percentage points increase in firm leverage ratio. The identification strategy employs a triple interaction of MSA-level land supply elasticity, real estate prices, and a measure of a firm's real estate holdings as an exogenous source of variation in firm collateral values. Firms significantly change their debt structure in response to collateral value appreciation. The results indicate the importance of collateral values in mitigating potential informational imperfections.

Araştırmanın önemi Türkiye'de çok yeni bir sermaye piyasası aracı olan gayrimenkul yatırım fonlarının (GYF) incelenmesidir. Bu amaçla; GYF'ler ve Türkiye'de GYF'lerin gelişimi ile mevcut durumu, ikincil veriler ve paydaş görüşmelerinin sonuçlarına dayalı olarak değerlendirilmiştir. Araştırma verilerinin toplanmasında derinlemesine görüşme tekniği kullanılmış olup, bu yöntemde yüz yüze ve birebir görüşmeler neticesinde yöneticilere araştırma ile ilgili açık uçlu sorular yöneltilmiş, GYF ve Türkiye'deki uygulama sorunları, GYF'lerin ülke ekonomisine, inşaat ve gayrimenkul sektörüne olan katkıları, avantaj ve dezavantajları incelenmiştir. Araştırma ile Türkiye'de henüz yeni sayılabilecek bu yeni yatırım aracının ilerleyen yıllarda daha da gelişeceği öngörülmüş olup gayrimenkule dayalı diğer araçlarda olduğu gibi GYF'lerin kuruluşu, değerlemesi, risk ve yatırım analizleri konularında çalışan Gayrimenkul Geliştirme ve Yönetimi uzmanlarının artırılması ve fonların paydaşları olan kurumlarda istihdam edilmelerinin sağlanması ile sorun çözümleme ve fonların gelişimine katkı sağlanmasının mümkün olabileceği ortaya konulmuştur.

Abstract Real estate crowdfunding is a relatively new alternative financing and investing method. This research aims to identify factors which might increase investors' willingness to participate in real estate crowdfunding campaigns. We analyse 195 lending-based real estate crowdfunding campaigns from four Spanish platforms. Project success is measured by duration, i.e. the time required to reach the funding target. We assess the impact of the funding target, the annual return, the loan duration, several risk-related metrics and the minimum investment amount. We find that the higher the funding target and the minimum investment amount, the longer it takes to reach the target. We document that investors prefer projects where the maturity of the loan is shorter. We also find that construction-type projects reach the funding target faster than other type of fundraising goals. At the same time, we do not find any association between the annual return or risk-related metrics and project success. To assure successful fundraising, real estate crowdfunding platforms should prioritize those real estate projects which are highly popular among investors (i.e. construction-type projects with short maturity). Real estate developers, in turn, should crowdfund projects which are demanded by the crowdinvestors and use their traditional financing methods for the remaining projects.

China's ongoing urban economic growth has sharply increased the population's per capita income, lowered the count of people living below the poverty line, and caused major environmental problems. We survey the growing literature investigating the causes and consequences of China's urban pollution challenges. We begin by studying how urban population and industrial growth impacts local pollution levels and greenhouse gas production. As the urban population grows richer, its demand for private transportation and electricity sharply increases. Such privately beneficial activity exacerbates urban pollution externalities. Facing these severe environmental challenges, China's urbanites increasingly demand quality of life progress. We survey the emerging literature investigating the demand for environmental progress in China. Progress in mitigating externalities hinges on whether the powerful central and local governments choose to address these issues. We analyze the political economy of whether government officials have strong incentives to tackle lingering urban externalities. We conclude by discussing future research opportunities at the intersection of environmental and urban economics.

We categorize Stockholm's urban green spaces according to the use values and social meanings they support, based on a sociotope mapping, and estimate their impact on property prices with a hedonic pricing model. The approach allows us to identify the most and least desired green space characteristics (attributes) and to assess the willingness to pay for the multifunctionality of green spaces. To do this, we test the following hypotheses, each with a separate hedonic pricing model: the proximity of all green space characteristics increases the property prices, but the specific monetary value of these characteristics differs;the multifunctionality of green spaces is well recognized and highly valued by real estate buyers. We find partial support for the first hypothesis: the green space attributes of \"aesthetics\", \"social activity\" and \"nature\" seem to be desired by real estate buyers, whereas \"physical activity\" and \"play\" seem not to be desired. We also find support for the second hypothesis: the higher the number of characteristics an urban green space has, the stronger its impact on property prices. This study furthers the discussion on the economic value of urban green spaces by assigning monetary value to their perceived character and use values. In doing so, it highlights the need to understand green spaces both as ecological features and social constructs.

Real estate is the third-largest asset class for institutional investors, but determining the value of commercial real estate assets remains elusively hard. In this article, the authors provide a practical application of big data by employing a unique set of data on U.S. multifamily assets, in combination with sophisticated modeling techniques, to develop an automated, machine-based valuation model for the commercial real estate sector. The authors find strong evidence of the superiority of automated valuation models over traditional appraisals: The absolute error of the automated model is 9%, which compares favorably against the accuracy of traditional appraisals, and the model can produce an instant value at every moment in time at a very low cost. The authors also provide evidence of the importance of using hyperlocal information on the location of an asset. The model developed in this article is directly applicable for real estate lenders and investors and has important implications for the traditional appraisal industry.

Abstract With the strengthening of macro-control of the real estate industry and the intensification of market competition, it is of great significance for the steady development of the industry to establish an accurate and effective early-warning mechanism for enterprise financing risk. Taking the A-share real estate listed companies in China in 2019 as target, this paper collects financial information of the relevant companies from 2010 to 2019, supplemented the risk sample data from 2005 to 2010, and reduced classification imbalance by removing discrete points and using SMOTE. When the financing risk evaluation system of listed real estate companies is constructed with capital as the entry point, the stochastic forest algorithm is used to finally select five important characteristic dimensions, namely, current ratio, equity financing ratio, operating income, current liability ratio and receivable turnover ratio. This paper establishes SVM early warning model, PSO-GA-BP optimization model and KNN early warning model to predict financing risks of real estate companies. It is concluded that, by comparing their applicability and advantages and disadvantages, SVM after feature screening and sample processing has better performance in the real estate financing risk early warning, which can provide some references for enterprise decision makers, investors and regulatory authorities.

The first hints of trouble in the mortgage market surfaced in mid-2005, and conditions subsequently began to deteriorate rapidly. Mortgage defaults and delinquencies are particularly concentrated among borrowers whose mortgages are classified as “subprime” or “near-prime.” The main factors underlying the rise in mortgage defaults appear to be declines in house prices and deteriorated underwriting standards, in particular an increase in loan-to-value ratios and in the share of mortgages with little or no documentation of income. Contrary to popular perception, the growth in unconventional mortgages products, such as those with prepayment penalties, interest-only periods, and teaser interest rates, does not appear to be a significant factor in defaults through mid-2008 because borrowers who had problems with these products could refinance into different mortgages. However, as markets realized the extent of the poor underwriting, underwriting standards tightened and borrowers began to face difficulties refinancing; this dynamic suggests that these unconventional products could pose problems going forward.

In this paper, we develop a DSGE model including heterogeneous households, introduce the financial friction of credit constraint mechanism, and study the impact of house price shocks on the consumption of heterogeneous household. Based on this, the CHFS data in 2011, 2013, 2015, 2017, and 2019 were used to test the marginal propensity to consume for housing wealth appreciation under different credit constraints. Results show that: Firstly, the financial accelerator mechanism plays an important role in the transmission of housing price shocks to household consumption. The looser the degree of credit constraints, the more obvious the rise in housing prices will be to the consumption expenditure of borrowing household. Secondly, the impact of housing wealth appreciation on household consumption under different credit constraints is heterogeneous. Among them, housing wealth appreciation has a significant positive impact on household consumption expenditure with multiple houses, credit cards, non-loan restrictions, while the marginal effect on the consumption expenditure of households with only one house, loan limited, and no credit cards decreases. Thirdly, for every 1% increase in the housing wealth appreciation, household consumption will increase significantly by 0.10-0.14%.