Parametric,semiparametric, and nonparametric estimation of characteristic values within mass assessment and hedonic pricing models

Parametric estimators, such as OLS, attain high efficiency for well-specified models. Nonparametric estimators greatly reduce specification error but at the cost of efficiency. Semiparametric estimators compromise between these dual goals of efficiency and specification error. Semiparametric estimators can assume general forms within classes of functional forms. This paper applies OLS, the kernel nonparametric regression estimator, and the semi-parametric estimator of Powell, Stock, and Stoker (1989) to a data set, which should, based on theory and previous empirical work, yield positive coefficients. The semiparametric estimator, on average, displayed the performance most consistent with prior expectations followed by the nonparametric and parametric estimators. In addition, the paper shows how the semiparametric estimator can provide insights into the form of misspecification and suggest data transformations.     

On the usefulness of the contrarian strategy across national stock markets: A grid bootstrap analysis

This paper statistically evaluates the usefulness of the contrarian investment strategy across the national stock markets of 18 developed countries. The contrarian strategy implicitly assumes that asset prices tend toward a fundamental value path over time. Conventional bootstrap analyses and panel unit root tests are often consistent with such a hypothesis. However, these results might be contaminated by small-sample bias and/or by not controlling cross-section dependence. Correcting for small-sample bias nonparametrically, I find extremely slow mean reversion rates, which provide strong evidence against the usefulness of the contrarian strategy.     

Dwelling Age,Redevelopment, and Housing Prices: The Case of Apartment Complexes in Seoul

The aging of a housing structure not only leads to depreciation but also increases the possibility of redevelopment. If redevelopment accompanies an increase in structural density in order to accommodate the increased demand for housing, it provides large capital gains to the existing owners. In this case, expectations of redevelopment in the near future and the eventual announcement of redevelopment plans can have a strong positive impact on the current price of housing. We test this hypothesis using a hedonic pricing model designed to decompose the age effects into depreciation effect and redevelopment effect. Based on 3,474 observations on apartments in Seoul in 2001, estimation results confirm our hypothesis. While the depreciation effect dominates the redevelopment effect until 15 to 19 years of age, depending on the specification, the redevelopment effect eventually dominates the depreciation effect thereafter, causing the apartment price to increase. At 27 years of age, the apartment price decreases by as much as 4553 percent of the initial value, due to depreciation. However, the redevelopment effect increases the price by as much as 2832 percent, driving the price up to 7687 percent of the original value.     

Models for Spatially Dependent Missing Data

Most hedonic pricing studies using transaction data employ only sold properties. Since the properties sold during any year or even decade represent only a fraction of all properties, this approach ignores the potentially valuable information content of unsold properties which have known characteristics. In fact, explanatory variable information on house characteristics for all properties, sold and unsold, are often available from assessors. We set forth an estimation approach that predicts missing values of the dependent variable when the sample data exhibit spatial dependence. Employing information on the housing characteristics of both sold and unsold properties can improve prediction, increase estimation efficiency for the missing-at-random case, and reduce self-selection bias in the non-missing-at-random case. We demonstrate these advantages with a Monte Carlo experiment as well as with actual housing data.     

Two-dimensional risk-neutral valuation relationships for the pricing of options

The Black–Scholes model is based on a one-parameter pricing kernel with constant elasticity. Theoretical and empirical results suggest declining elasticity and, hence, a pricing kernel with at least two parameters. We price European-style options on assets whose probability distributions have two unknown parameters. We assume a pricing kernel which also has two unknown parameters. When certain conditions are met, a two-dimensional risk-neutral valuation relationship exists for the pricing of these options: i.e. the relationship between the price of the option and the prices of the underlying asset and one other option on the asset is the same as it would be under risk neutrality. In this class of models, the price of the underlying asset and that of one other option take the place of the unknown parameters.      

Currency Hedging Using the Mean-Gini Framework

The mean-Gini framework has been suggested as a robust alternative to the portfolio approach to futures hedging given its optimality under general distributional conditions. However, calculation of the Gini hedge ratio requires estimation of the underlying price distribution. We estimate minimum-Gini hedge ratios using two widely-used estimation procedures, the empirical distribution function method and the kernel method, for three emerging market and three developed market currencies. We find that these methods yield different Gini hedge ratios. These differences increase with risk aversion and are statistically significant for all developed market currencies but only one emerging market currency. In-sample analyses show that the empirical distribution function method is more effective at risk reduction than the kernel method for developed market currencies, whereas the kernel method is superior for emerging market currencies. Post-sample analyses strengthen the superiority of the empirical distribution function method for developed market and, in several cases, for emerging market currencies.JEL Classification: F31, G15     

The Influence of Riparian Protection Measures on Residential Property Values: The Case of the Oregon Plan for Salmon and Watersheds

The Oregon Plan for Salmon and Watersheds encourages residential property owners to plant riparian buffers in an effort to reduce stream temperature and thus improve fish habitat. This study estimates the change in the value of streamside residential properties in response to planting a treed riparian buffer. A hedonic pricing analysis suggests that treed riparian buffers reduce the market value of stream-front residential property in the study area.     

The avenue effect,landmark externalities,and cubic transformation: Manhattan office valuation

A functional form for the valuation of office buildings is a third-order function that replicates the constrained cost function in which one of the inputs, land, is fixed. The third-order transformation improves a hedonic regression pricing model and reduces the autocorrelation. The presence of neighborhood landmarks, selected avenue address, and building size influence the office building value. The data set includes 103 midtown Manhattan office building sales transactions from 1980 to first quarter 1990.     

Implied Foreign Exchange Risk Premia

This paper uses implied volatilities from foreign exchange option prices and the results of no‐arbitrage theory to estimate foreign exchange risk premia. In particular, under the assumption of no‐arbitrage, the foreign exchange risk premium is driven by the difference between investors’ market prices of risk in the two currencies. In an international economy with three currencies, sterling, US dollar and Deutschemark, we can use the information on implied volatilities of the three cross rates to derive estimates of implied or ex ante market prices of risk and of foreign exchange risk premia. The foreign exchange risk premia estimates are then compared to survey‐based risk premia.     

Simultaneous estimation of the demand and supply of differentiated audits

This study shows how the supply and demand for auditing services is analyzed in a simultaneous equations framework. An important aspect of the analysis is the assumption that an audit is a differentiated product that is valued for its productive attributes. A hedonic (multiattribute), nonlinear fee function defines the fees that clear the market for all audit packages traded. Analysis of marginal fees and quantities of audit attributes transacted requires simultaneous estimation of a supply and demand function for each attribute. Several approaches for estimating the hedonic fee function and achieving parameter identification in the underlying structural equations are suggested.     

Asset pricing under information with stochastic volatility

Based on a general specification of the asset specific pricing kernel, we develop a pricing model using an information process with stochastic volatility. We derive analytical asset and option pricing formulas. The asset prices in this rational expectations model exhibit crash-like, strong downward movements. The resulting option pricing formula is consistent with the strong negative skewness and high levels of kurtosis observed in empirical studies. Furthermore, we determine credit spreads in a simple structural model.      

Semiparametric estimation of a characteristic-based factor model of common stock returns

We introduce an alternative version of the Fama–French three-factor model of stock returns together with a new estimation methodology. We assume that the factor betas in the model are smooth nonlinear functions of observed security characteristics. We develop an estimation procedure that combines nonparametric kernel methods for constructing mimicking portfolios with parametric nonlinear regression to estimate factor returns and factor betas simultaneously. The methodology is applied to US common stocks and the empirical findings compared to those of Fama and French.     

Option Prices Under Generalized Pricing Kernels

In this paper analytical solutions for European option prices are derived for a class of rather general asset specific pricing kernels (ASPKs) and distributions of the underlying asset. Special cases include underlying assets that are lognormally or log-gamma distributed at expiration date T. These special cases are generalizations of the Black and Scholes (1973) option pricing formula and the Heston (1993) option pricing formula for non-constant elasticity of the ASPK. Analytical solutions for a normally distributed and a uniformly distributed underlying are also derived for the class of general ASPKs. The shape of the implied volatility is analyzed to provide further understanding of the relationship between the shape of the ASPK, the underlying subjective distribution and option prices. The properties of this class of ASPKs are also compared to approaches used in previous empirical studies. JEL Classification: G12, G13, C65 Erik Lüders is an assistant professor at Laval University and a visiting scholar at the Stern School of Business, New York University.     

The Pricing of Lake Lots

This article reveals aspects of lakefront property pricing especially with respect to lot frontage and depth. A clearer understanding of how these lot dimensions affect price should be of interest to those engaged in lake development, land use control, valuation, and marketing. A data set with eighty observations of vacant Lake Michigan residential properties sales is used. The unique geography of northwest Michigan provides an opportunity to tackle empirical issues associated with zoning when zoning is correlated with lot attributes, such as lot topography.     

Testing conditional factor models

Using nonparametric techniques, we develop a methodology for estimating and testing conditional alphas and betas and long-run alphas and betas, which are the averages of conditional alphas and betas, respectively, across time. The estimators and tests can be implemented for a single asset or jointly across portfolios. The traditional Gibbons, Ross, and Shanken (1989) test arises as a special case of no time variation in the alphas and factor loadings and homoskedasticity. As applications of the methodology, we estimate conditional CAPM and multifactor models on book-to-market and momentum decile portfolios. We reject the null that long-run alphas are equal to zero even though there is substantial variation in the conditional factor loadings of these portfolios.     

A New Control Variate Estimator for an Asian Option

There exist several estimators for valuing the Asian option on the arithmetic mean. Among all variance reduction estimators, the one with the control variate derived from the geometric mean has been shown by Boyle et al. (1997) to perform best so far. In this paper, a new improved control variate estimator for this type of Asian option is proposed and investigated. Simulation results confirm that it does perform better than the control variate derived from the geometric mean. The improvement becomes more significant as the volatility increases and/or as the time to expiration lengthens.     

Estimation employing a priori information within mass appraisal and hedonic pricing models

Both statistical appraisal and hedonic pricing models decompose houses into a set of individual characteristics. Regression estimates yield the contribution of each characteristic to total value. Unfortunately, straightforward application of OLS may produce untenable results such as implausible coefficient magnitudes or incorrect signs. Often the suspected cause is multicollinearity. This article examines the effect on estimation efficiency of differing levels of multicollinearity, R², and a priori information in the form of sub-market cost data, by comparing inequality restricted least squares (IRLS) with OLS in a series of Monte Carlo experiments. The IRLS procedure investigated here hybridizes the statistical market approach implemented by OLS, and the more traditional cost approach. The experiments show dramatic gains in estimation efficiency from exploiting a priori information through IRLS.     

Option Pricing Bounds and the Elasticity of the Pricing Kernel

In this paper we use power functions as pricing kernels to derive option-pricing bounds. We derive option pricing bounds given the bounds of the elasticity of the true pricing kernel. The bounds of the elasticity of the true pricing kernel are closely related to the bounds of the representative investor's coefficient of relative risk aversion. This methodology produces a tighter upper call option bound than traditional approaches. As a special case we show how to use the Black–Scholes formula to obtain option pricing bounds under the assumption of lognormality.