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The purpose of this research is to develop and test a mathematical method of deriving zero yield curve from market prices of government bonds.

The method is based on a forward curve approximated by a linear (or piecewise constant) spline and should be applicable even for markets with low liquidity. The best fitting curve is derived by minimizing the penalty function. The penalty is defined as a sum of squared price discrepancies (theoretical curve based price minus market closing price) weighted by trade volume and an additional penalty for non‐smoothness of the yield curve.

This method is applied to both nominal and CPI linked bonds traded in Israel (some segments of these markets have low liquidity). The resulting two yield curves are used for derivation of market expected inflation rate.

The main problems are low liquidity of some bonds and imperfect linkage to inflation in the CPI linked market.

A stable numerical procedure applicable even in markets with low liquidity.

Usage of forward curves as the state space for the minimization problem leads to a stable solution that fits the data very well and can be used for calculating forward rates.

This paper aims to demonstrate how hospitality management research could benefit from the propositional style of theorizing, and how this approach could expand the scope of research in the discipline.

Developing new theories could provide unique insights and broaden the scope of research in hospitality management. To illustrate the power of proposition-based theorizing, this methodology is applied to the hotel cancellation policies domain.

Using the proposition-based theorizing in the context of cancellation policies, this study provides several propositions that could have broad implications for future research.

The contribution of this paper is threefold. First, the potential benefit of the proposition-based theorizing in the revenue management context of cancellation policies is demonstrated. Second, the theoretical frameworks and insights from the product return policy literature that could enrich future studies on hotel cancellation policies are introduced. Finally, this study conjectures on these theories’ relevance to hotel cancellation policies and consequently on their potential contribution to the scholarly discourse.

The purpose of this paper is to determine the key characteristics that determine housing prices in the USA. Data analytical models capable of predicting the driving forces of housing prices can be extremely useful in the built environment and real estate decision-making processes.

A data set of 13,771 houses is extracted from the 2013 American Housing Survey (AHS) data and used to develop a Hedonic Pricing Method (HPM). Besides, a data set of 22 houses in the city of San Francisco, CA is extracted from Redfin real estate brokerage database and used to test and validate the model. A correlation analysis is performed and a stepwise regression model is developed. Also, the best subsets regression model is selected to be used in HPM and a semi-log HPM is proposed to reduce the problem of heteroscedasticity.

Results show that the main driving force for housing transaction price in the USA is the square footage of the unit, followed by its location, and its number of bathrooms and bedrooms. The results also show that the impact of neighborhood characteristics (such as distance to open spaces and business centers) on the housing prices is not as strong as the impact of housing unit characteristics and location characteristics.

An important limitation of this study is the lack of detailed housing attribute variables in the AHS data set. The accuracy of the prediction model could be increased by having a greater number of information regarding neighborhood and regional characteristics. Also, considering the macro business environment such as the inflation rate, the interest rates, the supply and demand for housing, and the unemployment rates, among others could increase the accuracy of the model. The authors hope that the presented study spurs additional research into this topic for further investigation.

The developed framework which is capable of predicting the driving forces of housing prices and predict the market values based on those factors could be useful in the built environment and real estate decision-making processes. Researchers can also build upon the developed framework to develop more sophisticated predictive models that benefit from a more diverse set of factors.

Finally, predictive models of housing price can help develop user-friendly interfaces and mobile applications for home buyers to better evaluate their purchase choices.

Identification of the key driving forces that determine housing prices on real-world data from the 2013 AHS, and development of a prediction model for housing prices based on the studied data have made the presented research original and unique.