Same difference: A unifying framework for house value predictions through hedonic pricing, nearest neighbours, and comparable sales.

This paper focuses on forecasting the price of Bitcoin, motivated by its market growth and the recent interest of market participants and academics. We deploy six machine learning algorithms (e.g., Artificial Neural Network, Support Vector Machine, Random Forest, k-Nearest Neighbours, AdaBoost, Ridge regression), without deciding a priori which one is the ‘best’ model. The main contribution is to use these data analytics techniques with great caution in the parameterization, instead of classical parametric modelings (AR), to disentangle the non-stationary behavior of the data. As soon as Bitcoin is also used for diversification in portfolios, we need to investigate its interactions with stocks, bonds, foreign exchange, and commodities. We identify that other cryptocurrencies convey enough information to explain the daily variation of Bitcoin’s spot and futures prices. Forecasting results point to the segmentation of Bitcoin concerning alternative assets. Finally, trading strategies are implemented.

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Computing Expectiles Using k-Nearest Neighbours Approach

Symmetry ◽

10.3390/sym13040645 ◽

2021 ◽

Vol 13 (4) ◽

pp. 645

Author(s):

Muhammad Farooq ◽

Sehrish Sarfraz ◽

Christophe Chesneau ◽

Mahmood Ul Hassan ◽

Muhammad Ali Raza ◽

...

Keyword(s):

Computational Cost ◽

Real Life ◽

Distance Measures ◽

Computational Time ◽

High Dimensional ◽

Test Error ◽

Nearest Neighbours ◽

Comparable Performance ◽

Asymmetric Least Squares ◽

Low Computational Cost

Expectiles have gained considerable attention in recent years due to wide applications in many areas. In this study, the k-nearest neighbours approach, together with the asymmetric least squares loss function, called ex-kNN, is proposed for computing expectiles. Firstly, the effect of various distance measures on ex-kNN in terms of test error and computational time is evaluated. It is found that Canberra, Lorentzian, and Soergel distance measures lead to minimum test error, whereas Euclidean, Canberra, and Average of (L1,L∞) lead to a low computational cost. Secondly, the performance of ex-kNN is compared with existing packages er-boost and ex-svm for computing expectiles that are based on nine real life examples. Depending on the nature of data, the ex-kNN showed two to 10 times better performance than er-boost and comparable performance with ex-svm regarding test error. Computationally, the ex-kNN is found two to five times faster than ex-svm and much faster than er-boost, particularly, in the case of high dimensional data.

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Evaluating the Impact of Floods on Housing Price Using a Spatial Matching Difference-In-Differences (SM-DID) Approach

Sustainability ◽

10.3390/su13020804 ◽

2021 ◽

Vol 13 (2) ◽

pp. 804

Author(s):

Jean Dubé ◽

Maha AbdelHalim ◽

Nicolas Devaux

Keyword(s):

Housing Price ◽

Hedonic Pricing ◽

Environmental Amenities ◽

Sale Price ◽

Difference In Differences ◽

Hedonic Pricing Model ◽

The Difference ◽

And Control ◽

The Impact ◽

The City

Many applications have relied on the hedonic pricing model (HPM) to measure the willingness-to-pay (WTP) for urban externalities and natural disasters. The classic HPM regresses housing price on a complete list of attributes/characteristics that include spatial or environmental amenities (or disamenities), such as floods, to retrieve the gradients of the market (marginal) WTP for such externalities. The aim of this paper is to propose an innovative methodological framework that extends the causal relations based on a spatial matching difference-in-differences (SM-DID) estimator, and which attempts to calculate the difference between sale price for similar goods within “treated” and “control” groups. To demonstrate the potential of the proposed spatial matching method, the researchers present an empirical investigation based on the case of a flood event recorded in the city of Laval (Québec, Canada) in 1998, using information on transactions occurring between 1995 and 2001. The research results show that the impact of flooding brings a negative premium on the housing price of about 20,000$ Canadian (CAN).

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On the Determinants of House Value Volatility

Journal of Real Estate Research ◽

10.1080/10835547.2010.12091292 ◽

2010 ◽

Vol 32 (4) ◽

pp. 377-396 ◽

Cited By ~ 7

Author(s):

Yu Zhou ◽

Donald Haurin

Keyword(s):

House Value

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Hedonic Pricing of Milk Components at the Farm Level

Applied Economic Perspectives and Policy ◽

10.2307/1349431 ◽

1996 ◽

Vol 18 (2) ◽

pp. 181-192 ◽

Cited By ~ 4

Author(s):

William J. Gillmeister ◽

Robert D. Yonkers ◽

James W. Dunn

Keyword(s):

Hedonic Pricing ◽

Farm Level ◽

Milk Components

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Functional Form Model Specification: An Application to Hedonic Pricing

Agricultural and Resource Economics Review ◽

10.1017/s1068280500008807 ◽

1995 ◽

Vol 24 (2) ◽

pp. 166-173 ◽

Cited By ~ 10

Author(s):

Jeff E. Brown ◽

Don E. Ethridge

Keyword(s):

Functional Form ◽

Hedonic Pricing ◽

Hedonic Model ◽

Model Specification ◽

Estimation Accuracy ◽

Hedonic Price ◽

Partial Regression ◽

Cotton Price ◽

Residuals Analysis ◽

Form Model

A combination of conceptual analysis and empirical analysis—partial regression and residuals analysis—was used to derive an appropriate functional form hedonic price model. These procedures are illustrated in the derivation of a functional form hedonic model for an automated, econometric daily cotton price reporting system for the Texas-Oklahoma cotton market. Following conceptualization to deduce the general shapes of relationships, the appropriate specific functional form was found by testing particular attribute transformations identified from partial regression analysis. Minimizing structural errors across attribute levels and estimation accuracy were used in determining when an appropriate functional form for both implicit and explicit prices was found.

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Optimal Relabeling of Water Molecules and Single-Molecule Entropy Estimation

Biophysica ◽

10.3390/biophysica1030021 ◽

2021 ◽

Vol 1 (3) ◽

pp. 279-296

Author(s):

Federico Fogolari ◽

Gennaro Esposito

Keyword(s):

Single Molecule ◽

Degrees Of Freedom ◽

Water Molecules ◽

Maximum Information ◽

Nearest Neighbours ◽

Biomolecular Simulations ◽

Solvent Molecules ◽

Solvation Entropy ◽

Dynamics Simulations ◽

Internal Degrees Of Freedom

Estimation of solvent entropy from equilibrium molecular dynamics simulations is a long-standing problem in statistical mechanics. In recent years, methods that estimate entropy using k-th nearest neighbours (kNN) have been applied to internal degrees of freedom in biomolecular simulations, and for the rigorous computation of positional-orientational entropy of one and two molecules. The mutual information expansion (MIE) and the maximum information spanning tree (MIST) methods were proposed and used to deal with a large number of non-independent degrees of freedom, providing estimates or bounds on the global entropy, thus complementing the kNN method. The application of the combination of such methods to solvent molecules appears problematic because of the indistinguishability of molecules and of their symmetric parts. All indistiguishable molecules span the same global conformational volume, making application of MIE and MIST methods difficult. Here, we address the problem of indistinguishability by relabeling water molecules in such a way that each water molecule spans only a local region throughout the simulation. Then, we work out approximations and show how to compute the single-molecule entropy for the system of relabeled molecules. The results suggest that relabeling water molecules is promising for computation of solvation entropy.

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