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The purpose of this paper is to estimate that price appreciation for single‐family sales data for the complete population of deed recordings in one metro area using median comparisons is statistically more accurate in capturing local fluctuations than the repeat sale sample approach published by the Federal Housing Finance Agency. The median comparison method becomes the optimal method of choice due to its simplicity and ease in interpretation. The electronic access to court house records means that the complete population of data should be used to extract a community price trend in lieu of samples.

Local deed recordings for residential housing were compared to repeat sale results from the Federal Finance Housing Admin. The goal is to measure housing price appreciation.

Appreciation measured by local deed recordings captures house price variance better than repeat sales taken from mortgage applications.

A median to median comparison with local sales prices should be used in lieu of a national statistic using mortgage applications.

This project provides a more accurate method to assess price appreciation. Repeat sales are not appropriate especially in smaller metro areas.

Every individual and financial institution needs to know the value of its housing asset. The method shown is the best available.

The results have huge implications for the academic community where many regression equations are based on repeat sales.

This paper aims to compare a median‐to‐median estimate of residential price change to the FHFA home price index composed of repeat sales. Further, it seeks to use the complete population of closed sales. The conceptual issue is the use of a “typical” or average comparison through time as opposed to properties where attributes and their marginal prices are held constant.

The paper uses the total population of closed sales rather than a sample. A time series for median price changes is compared to the FHFA time series. The medians for the complete population are the benchmarks, as the median parameter is the true value.

The study finds that the quarterly FHFA price changes do not capture market movements following a major external shock such as a tropical storm. Further, the FHFA data originate in mortgage applications which are not characteristic of the local market. The conclusion is that a median comparison is better, all deed recordings should be used, and repeat sales are not always a valid tool.

Acquiring the data set of all deed transactions involved a budget which may be available to all analysts.

The practical applications are enormous. The results cast doubts on the FHFA home price index and the Case Shiller index. The paper supports the method used by the National Association of Realtors.

All researchers interested in local real estate markets are concerned about the best method to measure changes in local demand and supply market conditions. This project presents a method to use that is sound conceptually and statistically. The reason is that the goal is to measure changes in the “typical”, or average, property over time.

The literature abounds with repeat sale papers. This paper gives an alternative and avoids the many flaws with paired sales. It should have a wide readership.

Wellbeing evaluation using ordered categorical response data is hazardous given the scale dependent nature of most measures of wellbeing and inequality. Here, scale independent instruments for measuring levels of wellbeing and inequalities between groups in multidimensional ordered categorical environments are introduced and applied in a study of health and consumption wellbeing and the aging process in twenty‐first century China. Urban/rural location, gender, age and the availability of welfare support were considered circumstances in what is in essence a study of equality of opportunity in the acquisition of health and consumption wellbeing in Chinas’ aging population. Older populations are found to experience diminished and increasingly diverse wellbeing outcomes that are, to some extent, ameliorated by welfare support.

This paper aims to analyze how political brinkmanship impacted Treasury yields during the debt ceiling debate in 2015. The results show that the resignation of the House Speaker John A. Boehner caused a significant decrease in Treasury bill yields of one- and three-month maturities. The authors robust analysis indicates that these lower yields have saved US taxpayers several billion dollars in extra tax expenses. This paper provides evidence that lack of political brinkmanship can be very advantageous for the taxpayers. This has considerable implications for lawmakers in this post-election year.

The authors examine the differences in yields between equal maturity short-term Treasury securities and commercial paper using t-tests, non-parametric tests and a robust regression model based on earlier empirical studies.

This study provides evidence indicating that between September 25, 2015, and up to October 30, 2015, relatively lower Treasury yields resulted from the lack of political brinkmanship, and this has saved the US taxpayers several billion dollars in interest expenses in 2015.

The study showed that lower yields will result from a lack of political brinkmanship, and this resulted in savings of several billions of dollars in interest payments. Considering that both the White House and Congress will be controlled by the same political party, this gives lawmakers a unique opportunity to have less acrimonious debt ceiling debates. The limitation of the study is that it does not consider the impact on foreign exchange markets and other factors which could play a major role.

Unlike earlier scenarios where default risk increased, followed by credit rating downgrades, there was a quiet confidence this time about a quick resolution. Markets were stable, and this allowed money market participants to invest more confidently even when an upcoming debt ceiling debate is on. Corporations that invested additional cash in money markets for short-term could do it more confidently at that time without fear of default or interest rate risk which could potentially harm the market value of their investments.

It implies that there will be lower taxpayer costs because of debt ceilings and avoidance of shutdowns of the federal government. It also implies that there could be more confidence in the dollar.

Several earlier studies have examined Treasury default caused by political brinkmanship. This is the first study to examine an event where political brinkmanship appeared possible and then suddenly dissipated in a single day. Political brinkmanship is bad news because it increases taxpayer interest burden as seen from several of the studies above. Therefore, it should be considered good news if no disagreement is evident. This argument serves as our motivation for this paper. As an increase in the chances of default causes an increase in the yield of Treasury bills as earlier studies showed, a decrease in the chance of default caused Treasury bill yields to be that much lower based on the results of this study.

This paper aims to compare stochastic gradient method used for neural network training with global optimizer without use of gradient information, in particular differential evolution.

This contribute shows the application of heuristic optimization algorithms to the training phase of artificial neural network whose aim is to predict renewable power production as function of environmental variables such as solar irradiance and temperature. The training problem is cast as the minimization of a cost function whose degrees of freedom are the parameters of the neural network. A differential evolution algorithm is substituted to the more usual gradient-based minimization procedure, and the comparison of their performances is presented.

The two procedures based on stochastic gradient and differential evolution reach the same results being the gradient based moderately quicker in convergence but with a lower value of reliability, as a significant number of runs do not reach convergence.

The approach has been applied to two forecasting problems and, even if results are encouraging, the need for extend the approach to other problems is needed.

The new approach could open the training of neural network to more stable and general methods, exploiting the potentialities of parallel computing.

To the best of the authors’ knowledge, the research presented is fully original for the part regarding the neural network training with differential evolution.

The Project Management Body of Knowledge recommends the use of issue logs as a best practice to minimize the potential project cost and schedule growth. Although the broader topic of project control has been widely studied in the construction literature, the specific application of issue logs has remained relatively understudied. This study aims to analyze the extent and consistency with which construction teams utilize issue logs and the corresponding project performance outcomes.

A dataset of 5,635 individual issues was gathered from the final issue logs of 881 small building projects delivered via the design–bid–build method. Differences between groups were determined using the Kruskal–Wallis H test with post hoc testing via the Mann–Whitney U test with pairwise comparison.

The results showed that, on average, project teams who used issue logs to a greater extent achieved a 3.1 to 4.3% reduction in cost growth and a 5.3 to 12.3% reduction in schedule growth. This result shows that issue logs can be used to improve construction project performance in the areas of cost and schedule.

This result provides a contribution to practitioners, wherein project teams should be encouraged to establish their issue management practices early in the project schedule to encourage greater issue log usage for the remainder of the project.

This chapter provides a historical overview of the data and methodologies used to measure worklife and the usefulness of such statistics for projecting future retirement behavior. Section 2 discusses the calculation of worklife expectancy (WLE), beginning with a review of the data and method of calculation using what has come to be known as the conventional model. Section 3 looks at the WLE results using the increment–decrement model and subsequent models. Sections 4 and 5 revisit the conventional model and compare the results of the conventional model to the increment-decrement model. Section 6 of the chapter discusses the data and methodologies used to calculate years to final separation (YFS), and Section 7 discusses the use of both WLE and YFS statistics in projecting future retirement behavior.

The investment behaviour of individuals has been a major area of interest for several researchers and policymakers due to its great impact on the economy. This study aimed to assess the investment behaviour of individuals in light of their risk appetite and how financial literacy regulates this relationship.

A self-administered structured questionnaire was used to collect responses from individuals using purposive and convenience sampling techniques. Individuals were presented with 16 investment avenues widely offered by the Indian financial market to choose from to construct a hypothetical portfolio. The association between risk appetite, financial literacy and the composition of the hypothetical portfolio was analysed using a gologit model.

Increased risk appetite increased the probability of respondents creating a portfolio with a greater proportion of risky assets and less diversification. Lower levels of financial literacy pointed towards portfolios with traditional and low-risk avenues. The results also revealed a significant moderating impact of financial literacy on risk appetite and the creation of the type of a hypothetical portfolio.

Even though the intended behaviour is a close estimate of actual behaviour, there is a possibility of deviation that cannot be ignored.

The present study provides insights into how individuals make portfolio choices by incorporating risk appetite and diversification factors whilst making investment decisions, thereby expanding the literature from an emerging economy perspective. The role of financial literacy as a moderator has not been studied in the domain of hypothetical portfolio creation in India, which has been empirically explored in the current study.

This paper examines the opportunity cost of applying simple averages in formulating the Department of Defense (DoD) budget for foreign exchange rates. Using out-of-sample validation, we evaluate the status quo of a center-weighted average against a Random Walk model, ARIMA, forward rates, futures contracts, and a private firm's forecasts over two time periods extending from Fiscal Year (FY) 1991 to FY 2014. The results strongly indicate that four of the alternative methods outperform the status quo over the shorter time period, and three methods for both time periods. Furthermore, a non-parametric comparison of the median error demonstrates statistical similarities between the four alternative methods over the short term. Overall, the paper recommends using the futures option prices to decrease forecast error by 3.23% and avoiding a $34 million opportunity cost.