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The real estate industry is known as a late adopter when it comes to changes and innovations. While the industry is slowly evolving, parts of the sector are increasingly being conquered by property-related start-ups, known as “PropTechs”. These companies offer solutions and cutting-edge technologies to increase efficiencies and solve industry-wide problems. However, little is known about these companies' survival. This paper analyses the survival rate of PropTech firms and the determinants.

Based on a sample of 1,052 firms, factors that influence the firms' survival rate are analysed using the Cox Proportional Hazards Model, which is expanded with non-linear splines to capture turning points in the survival.

The authors find that in addition to the size, financing condition plays the most critical role in the success of Prop-Tech firms, including the number of financing rounds and maximum number of investors over lifetime. Moreover, the relationships are non-linear. Founding years and technology focus can also statistically influence the success rate. Companies founded before 2008 focussing on Sustainability Technology, Data and Business Analytics, Real Estate-related FinTech and Visualisation show the highest success rates.

The results are critical for investors interested in PropTechs to understand the success of their investments better. The importance of financing conditions shows that both investors and PropTechs may benefit from better financing processes that provide funds in a timelier manner.

The authors exploit a new and comprehensive data set that includes over 6,000 PropTechs globally. The authors' study fills in the literature gap on the determinants of the survival rate of PropTechs.

The purpose of this paper is to present a comprehensive framework for assisting lending banks in their current expected credit losses (CECL) forthcoming computations.

The bottom-up approach requires multiple steps including the spline method for identifying optimal segments in the lifetimes of loans, Poisson regressions for evaluating the explanatory variables and hazard rate probes for gaining inferences toward the expected credit losses and their projected schedule.

The CECL paradigm has both advantages and disadvantages, as discussed hereafter.

The model is practical, accurate in the sense that provisions are properly and timely allocated, it can be programmed and it relies on merely a few mild assumptions, thus it can be conveniently calibrated to fit broad macroeconomic scenarios.

This study provides background on the subject, motivate each module, construct the advised model, assemble a pseudo-database, demonstrate the functionality of the procedures and further draw conclusions on the effectiveness of the current strategy.

Survival (default) data are frequently encountered in financial (especially credit risk), medical, educational, and other fields, where the “default” can be interpreted as the failure to fulfill debt payments of a specific company or the death of a patient in a medical study or the inability to pass some educational tests.

This paper introduces the basic ideas of Cox's original proportional model for the hazard rates and extends the model within a general framework of statistical data mining procedures. By employing regularization, basis expansion, boosting, bagging, Markov chain Monte Carlo (MCMC) and many other tools, we effectively calibrate a large and flexible class of proportional hazard models.

The proposed methods have important applications in the setting of credit risk. For example, the model for the default correlation through regularization can be used to price credit basket products, and the frailty factor models can explain the contagion effects in the defaults of multiple firms in the credit market.

This study aims to explore the underlying patterns in tax innovation. Prior studies of local sales taxes still leave a gap in the literature and render the results inconclusive because the studies cover either state level or localities within a single state for a short period. To cover the gap, we assemble a dataset of counties in all states for FY1970-2006 but focus on 12 states not threatened by intra-jurisdictional competition. Our empirical analyses yield evidence that a county adopts local sales tax for political and economic rationale rather than fiscal condition. Accordingly, regional diffusion has positive effects on local sales tax adoption in a county. These findings contribute substantively to sales tax literature while confirming policy diffusion.

The purpose of this paper is to provide an effective way to assess landslide risk quantitatively. Quantitative assessment plays an important role in mitigating the landslide risk and developing a landslide risk-based warning system. However, efficient risk assessment on the large deformation failure process of slope with spatially variable soils is a challenging problem.

Combining the Monte Carlo simulation (MCS) and the higher-order material point method – the B-spline Material Point Method (BSMPM) – the concept of MC-BSMPM to assess the landslide risk quantitatively is proposed in this paper. The overall dynamic evolution of soil slope failure has been simulated by the BSMPM, and the probability density function of the sliding duration, the sliding kinematic energy, the sliding mass and the sliding distance of the landslide are obtained based on the MCS. Through the four risk assessment parameters of the sliding duration, the sliding kinematic energy, the sliding mass and the sliding distance, the landslide risk could be assessed quantitatively.

It is found that the post-failure behavior of the landslide conforms well to a normal distribution as the soil physical parameter is in a normal distribution. The variation of soil’s shear strength affects the dynamic motion of the landslide greatly.

The result shows that the landslide hazard cannot be estimated comprehensively by the deterministic BSMPM, while the landslide risk could be more clearly understood and quantitatively assessed with more details by the proposed method, which demonstrates that the MC-BSMPM method is an effective way to assess the landslide risk quantitatively.

Gives a bibliographical review of the finite element methods (FEMs) applied for the linear and nonlinear, static and dynamic analyses of basic structural elements from the theoretical as well as practical points of view. The range of applications of FEMs in this area is wide and cannot be presented in a single paper; therefore aims to give the reader an encyclopaedic view on the subject. The bibliography at the end of the paper contains 2,025 references to papers, conference proceedings and theses/dissertations dealing with the analysis of beams, columns, rods, bars, cables, discs, blades, shafts, membranes, plates and shells that were published in 1992‐1995.

The purpose of this paper is to determine the effects of the 2018–2020 China–US trade war on US farm bankruptcies as filed under Chapter 12. The key task is to identify the economic factors affecting farm bankruptcies generally, and to then control for the trade war impacts including the Market Facilitation Program (MFP), floods, agricultural conditions and the health of agricultural finance leading into the trade war.

Results were obtained using ordinary least square regression and panel fixed effect model using bankruptcy rates and number as the dependent variable. Independent variables included market effects, credit conditions, yield variation, trade impacts, 2019 flooding, macroeconomic conditions and regional fixed effects. The authors use cubic splines to interpolate annual and quarterly data to a monthly base.

Based on a fixed effect model, the authors find that all other things being equal the China–USA trade war would have had a significant impact on Chapter 12 farm bankruptcies, increasing the bankruptcy rate by 25.7%. The flooding in 2009 had minor effects of increasing the rate by only 0.05%. The overall impact will, however be substantially lower than the 25.7% because of the MFP. The MFP variables (binary) had mixed effects and its true impact is unknowable at this time; however, the authors also find that a 1% increase in the producer price index decreases bankruptcy rates by 2.62% and farm bankruptcy numbers by 3.70%. Likewise a 1% increase in GDP reduces bankruptcies by 3.25%. These suggest that the MFP program will have likely reduced farm bankruptcies considerably than what would have occurred in their absence. The authors also find that states heavily dependent on trade faced lower market uncertainty. Broader economic factors (net charge-offs of farm loans held by insured commercial banks, US real GDP, the average effective interest rate on nonreal estate farm loans) affect farm bankruptcy.

The authors use monthly bankruptcy statistics, however not all data were available in monthly measures requiring interpolation using cubic spline functions to approximate monthly changes in some variables. Although the MFP had mixed effects in the model, the mid- to longer-term effects may be more impactful. These longer-term effects (and even shorter-term effects through 2020) are complicated by the coronavirus disease 2019 (COVID-19) pandemic, which will require a different identification strategy than that employed in this paper.

The analysis and results of this paper are, to the authors' knowledge, the first to investigate the impact of the China–US trade war on Chapter 12 farm bankruptcy filings. The use of cubic splines in the interpolation of agricultural data is also a technical innovation.

This research aims to evaluate the accuracy of several Value-at-Risk (VaR) approaches for determining the Minimum Capital Requirement (MCR) for Islamic stock markets during the pandemic health crisis.

This research evaluates the performance of numerous VaR models for computing the MCR for market risk in compliance with the Basel II and Basel II.5 guidelines for ten Islamic indices. Five models were applied—namely the RiskMetrics, Generalized Autoregressive Conditional Heteroskedasticity, denoted (GARCH), fractional integrated GARCH, denoted (FIGARCH), and SPLINE-GARCH approaches—under three innovations (normal (N), Student (St) and skewed-Student (Sk-t) and the extreme value theory (EVT).

The main findings of this empirical study reveal that (1) extreme value theory performs better for most indices during the market crisis and (2) VaR models under a normal distribution provide quite poor performance than models with fat-tailed innovations in terms of risk estimation.

Since the world is now undergoing the third wave of the COVID-19 pandemic, this study will not be able to assess performance of VaR models during the fourth wave of COVID-19.

The results suggest that the Islamic Financial Services Board (IFSB) should enhance market discipline mechanisms, while central banks and national authorities should harmonize their regulatory frameworks in line with Basel/IFSB reform agenda.

Previous studies focused on evaluating market risk models using non-Islamic indexes. However, this research uses the Islamic indexes to analyze the VaR forecasting models. Besides, they tested the accuracy of VaR models based on traditional GARCH models, whereas the authors introduce the Spline GARCH developed by Engle and Rangel (2008). Finally, most studies have focus on the period of 2007–2008 financial crisis, while the authors investigate the issue of market risk quantification for several Islamic market equity during the sanitary crisis of COVID-19.

The aim of this paper is to show how provisions of the Supplemental Revenue Assistance Payments (SURE) program impacts production practices, and empirically examine changes in crop insurance participation rates as a means of measuring producer responses to the program.

The structure of the SURE program is described and a stylized theoretical model is used to show the SURE program's effects on farm‐level crop insurance and production decisions. A county‐level cross‐sectional empirical specification with regional fixed effects is used to test the hypothesis that producers who are most likely to benefit from production practice re‐optimization are more likely to participate in crop insurance.

Results from empirical analyses of corn, soybean, and wheat production areas show that the SURE program has had substantial impacts on crop insurance participation by producers who are more likely to receive SURE indemnities and exploit moral hazard opportunities.

Because the program has only recently been introduced, empirical estimates of the program's long‐run impacts are not estimable.

Results indicate that the program can have unexpected market consequences, with increased frequency and size of SURE indemnity claims than the Congressional Budget Office anticipated and increases in aggregate tax payer subsidies for both the crop insurance and SURE program. These outcomes can have important implications on motivating a restructuring of the program in the next farm bill.

Increased tax payer expenditures on the SURE and crop insurance programs in the form of subsidies can lead to non‐trivial reductions in social welfare.

This research is the first to develop a rigorous model of the SURE program's impacts on producer responses and associated effects on crop insurance participation. The study also provides empirical evidence of these effects.

The purpose of this paper is to estimate the impact of two policies (an extension of the waiting period before entitlement to unemployment insurance (UI) and an intensification of counselling) targeted at unemployed school-leavers in Belgium on unemployment duration and on the quality of work.

The length of both policies is sharply determined by two distinct age thresholds. These thresholds are exploited to estimate the impact within a regression discontinuity design using a large administrative data set of all recent labour market entrants.

The longer waiting period does not significantly impact job finding while the Youth Work Plan does increase the job-finding rate eight months after the onset of the programme. The accepted wage is unaffected, but both policies lower the number of working days resulting in lower earnings. This effect is especially prevalent for youth from low-income households.

For both policies, participation was delineated by an age cut-off which was only four months apart. This sizeably reduced the width of the age window to detect a corresponding discontinuity in behaviour and hereby also the statistical power of the estimator. Additionally, due to confounding policies the estimated effects are local treatment effects for highly educated youth around the age cut-offs.

The findings suggest that threatening with a sanction is not the right instrument to activate highly educated unemployed school-leavers. While supportive measures appear to be more effective, this may be partly a consequence of acceptance of lower quality jobs due to liquidity constraints and of caseworkers giving misleading advice that temporary jobs are stepping stones to long-term employment.

To the best of the authors’ knowledge, this is the first paper to estimate the impact of changing the waiting period in UI. The paper adds to the existing literature on the effects of counselling and UI design on employment and job quality.