Search results

The purpose of this paper is to present an algorithm of real estate mass appraisal in which the impact of attributes (real estate features) is estimated by inequality restricted least squares (IRLS) model.

This paper presents the algorithm of real estate mass appraisal, which was also presented in the form of an econometric model. Vital problem related to econometric models of mass appraisal is multicollinearity. In this paper, a priori knowledge about parameters is used by imposing restrictions in the form of inequalities. IRLS model is therefore used to limit negative consequences of multicollinearity. In ordinary least squares (OLS) models, estimator variances might be inflated by multicollinearity, which could lead to wrong signs of estimates. In IRLS models, estimators efficiency is higher (estimator variances are lower), which could result in better appraisals.

The final effect of the analysis is a vector of the impact of real estate attributes on their value in the mass appraisal algorithm. After making expert corrections, the algorithm was used to evaluate 318 properties from the test set. Valuation errors were also discussed.

Restrictions in the form of inequalities were imposed on the parameters of the econometric model, ensuring the non-negativity and monotonicity of real estate attribute impact. In case of real estate, variables are usually correlated. OLS estimators are then inflated and inefficient. Imposing restrictions in form of inequalities could improve results because IRLS estimators are more efficient. In the case of results inconsistent with theoretical assumptions, the real estate mass appraisal algorithm enables having the obtained results adjusted by an expert. This can be important for low quality databases, which is often the case in underdeveloped real estate markets. Another reason for expert correction may be the low efficiency of a given real estate market.

This article seeks to (1) identify forecasting techniques used to estimate taxable sales in California counties; (2) analyze which of these produces the most accurate estimate; (3) document what prevented officials from using the most accurate forecasting technique in California counties; and (4) determine what forecasting approach would work best for individual counties. This research generally confirms previous research findings that judgmental approaches are the most commonly used method of revenue forecasting in smaller localities. In terms of accuracy, econometric models outperform other quantitative methods, particularly compared to trend line fitting and extrapolation-by-average approaches. The “not now but later” perception in the use of econometric models can be ascribed to California county forecasters’ discomfort and lack of preparation for using this sophisticated technique. Once the critical prerequisites for the use of econometric models are provided -- such as statewide training, timely inter-governmental data sharing, easy access to economic data, and user-friendly forecasting formats with automated procedures -- econometric models can serve the needs of California counties.

This study uses the neural network and econometric models to explore the importance of fiscal and monetary policy on GNP. The findings suggest that fiscal policy is more influential than monetary policy, and the neural network forecasts of GNP are more accurate and have less variation than those of the econometric approach.

Fiscal stress has forced local governments to pay increasing attention to revenue trends and has increased the importance of financial forecasting in local government. After reviewing the role of revenue forecasting in financial planning and discussing the use of regression and econometric analysis in revenue forecasting, this article applies this technique to forecast several key revenue components in a medium-sized city. Three general conclusions may be drawn: (1) systematic revenue forecasting and long-range planning are necessities, not luxuries, (2) risk aversion to "technical" revenue forecasting can be overcome, and (3) the implementation of a systematic revenue forecasting system does not require a battery of "rocket scientists." As municipal revenue bases come to rely less on relatively stable property taxes and more on less stable sources such as sales taxes, fees, and charges, the use of a regression and econometric based model should prove increasingly fruitful.

Whereas investments in new attractions continue to rise within the theme park industry, knowledge regarding the effects of new attractions on theme park performance and attendance remains scarce. In order to isolate these effects, the purpose of this paper is to present the results of an econometric study explaining the variance in theme park visitor numbers and quantifying the effects of new attractions on theme park attendance.

The paper is based on an econometric study, in which models were produced for four European theme parks. No pooled modelling was used, meaning that four different models were created; one for each participating theme park. Various variables affecting theme park attendance were identified and quantified, and subsequently the effects of new attractions on visitor numbers were isolated.

Findings indicate that all new attractions opened at Park D during the research period have had a positive long‐term influence on attendance. This positive influence lasted for no more than two years. No significant short‐term influence was found. There were significant differences in effect between new attractions which could not yet be explained.

The research by design only takes into account the economic effects of new attractions and disregards all environmental and socio‐cultural effects. Even though the research provides an accurate approximation of the effects of new attractions on attendance, this effect should, according to the author, not be perceived as a stand‐alone effect yet as a part of a complex system. A situational approach taking into account several other situational as well as qualitative factors would do the complex reality more justice than a, even though effective, simplified and general approach.

Industry operators can now use the econometric model presented in this paper to determine the effects of new attractions on their theme park's attendance and use this knowledge to further fine‐tune their investment policy.

The paper presents the first econometric model successful at isolating and quantifying a new attraction's effect on theme park attendance and can thus be a valuable tool in perfecting one's investment policy. The paper furthermore includes a brief introduction to a situational approach of determining a new attraction's effects on theme park performance.

Analyses the influence of value judgements in the mechanics of testing econometric theories against empirical data. The orthodox view of mainstream, positive economics is that value judgements play no part in the above process. Contests this view; defines value judgements and shows the orthodox conception to be too narrow, compared with the meaning and use of the term in other disciplines. Reviews many published examples from the 1970s and 1980s and ways in which value judgements have affected testing procedures in economics. Hypothesis testing via econometric techniques is fraught with value judgements because the application of statistical methodology is not a determinate, neutral or objective process.

This paper is concerned with the state‐space approach to optimal control problems of dynamic econometric systems. We show how the state‐space approach can be integrated into the traditional econometric method, and how much could be gained by this consolidated approach.

Our research aims in designation of a hybrid approach in the calibration of an attribute impact vector in order to guarantee its completeness in case when other approaches cannot ensure this.

Real estate mass appraisal aims at valuating a large number of properties by means of a specialised algorithm. We can apply various methods for this purpose. We present the Szczecin Algorithm of Real Estate Mass Appraisal (SAREMA) and the four methods of calibration of an attribute impact vector. Eventually, we present its application on the example of 318 residential properties in Szczecin, Poland.

We compare the results of appraisals obtained with the application of the hybrid approach with the appraisals obtained for the three remaining ones. If the database is complete and reliable, the econometric and statistical approaches could be recommended because they are based on quantitative measures of relationships between the values of attributes and properties' unit values. However, when the database is incomplete, the expert and, subsequently, hybrid approaches are used as supplementary ones.

The application of the hybrid approach ensures that the calibration system of an attribute impact vector is always complete. This is because it incorporates the expert approach that can be used even if the database excludes application of approaches that are based on quantitative measures of relationship between the unit real estate value and the value of attributes.

The purpose of this paper is to present how prior knowledge about the impact of real estate features on value might be utilised in the econometric models of real estate appraisal. In these models, price is a dependent variable and real estate features are explanatory variables. Moreover, these kinds of models might support individual and mass appraisals.

A mixed estimation procedure was discussed in the research. It enables using sample and prior information in an estimation process. Prior information was provided by real estate experts in the form of parameter intervals. Also, sample information about the prices and features of undeveloped land for low-residential purposes was used. Then, mixed estimation results were compared with ordinary least squares (OLS) outcomes. Finally, the estimated econometric models were assessed with regard to both formal criteria and valuation accuracy.

The OLS results were unacceptable, mostly because of the low quality of the database, which is often the case on local, undeveloped real estate markets. The mixed results are much more consistent with formal expectations and the real estate valuations are also better for a mixed model. In a mixed model, the impact of each real estate feature could be estimated, even if there is no variability in the sample information. Valuations are also more precise in terms of their consistency with market prices. The mean error (ME) and mean absolute percentage error (MAPE) are lower for a mixed model.

The crucial problem in econometric property valuation is that it involves the unreliability of databases, especially on undeveloped, local markets. The applied mixed estimation procedure might support sample information with prior knowledge, in the form of stochastic restrictions imposed on parameters. Thus, that kind of knowledge might be obtained from real estate experts, practitioners, etc.

The experiential nature of travel and tourism services has popularized the importance of electronic word-of-mouth (EWOM) among potential customers. EWOM has a significant influence on hotel booking intention of customers as they tend to trust EWOM more than the messages spread by marketers. Amid abundant reviews available online, it becomes difficult for travelers to identify the most significant ones. This questions the credibility of reviewers as various online businesses allow reviewers to post their feedback using nickname or email address rather than using real name, photo or other personal information. Therefore, this study aims to determine the factors leading to reviewer credibility.

The paper proposes an econometric model to determine the variables that affect the reviewer’s credibility in the hospitality and tourism sector. The proposed model uses quantifiable variables of reviewers and reviews to estimate reviewer credibility, defined in terms of proportion of number of helpful votes received by a reviewer to the number of total reviews written by him. This covers both aspects of source credibility i.e. trustworthiness and expertness. The authors have used the data set of TripAdvisor.com to validate the models.

Regression analysis significantly validated the econometric models proposed here. To check the predictive efficiency of the models, predictive modeling using five commonly used classifiers such as random forest (RF), linear discriminant analysis, k-nearest neighbor, decision tree and support vector machine is performed. RF gave the best accuracy for the overall model.

The findings of this research paper suggest various implications for hoteliers and managers to help retain credible reviewers in the online travel community. This will help them to achieve long term relationships with the clients and increase their trust in the brand.

To the best of authors’ knowledge, this study performs an econometric modeling approach to find determinants of reviewer credibility, not conducted in previous studies. Moreover, the study contracts from earlier works by considering it to be an endogenous variable, rather than an exogenous one.