Rent index forecasting through neural networks

2023

ECON

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PurposeForecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.Design/methodology/approachThe authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.FindingsThe final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.Originality/valueThrough the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

Section: Methodsmentioning

confidence: 99%

Yellow corn wholesale price forecasts via the neural network

2023

ECON

Self Cite

“…It uses sigmoid transfer functions among hidden layers and a linear transfer function for the output layer. We consider both the Levenberg-Marquardt (LM) [269,270] and scaled conjugate gradient (SCG) [271] algorithms for model training, both of which have been found in the literature to be powerful and useful across a variety of research areas [119,[272][273][274][275][276][277][278][279]. More comparative studies of these two algorithms might be located in some of previous work [280][281][282][283][284].…”

Section: Methodsmentioning

confidence: 99%

Scrap steel price forecasting with neural networks for east, north, south, central, northeast, and southwest China and at the national level

Ironmaking & Steelmaking

2023

Self Cite

There is little doubt about importance of forecasting commodity prices to policy makers and diverse varieties of market participants. In this present work, we analyse price forecasting problems for scrap steel for east, north, south, central, northeast, and southwest China and at the national level with daily data during 08/23/2013-04/15/2021. We focus on exploration of usefulness of non-linear autoregressive neural networks and examine forecasting performance based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays, and ratios for splitting the data into training, validation, and testing phases. We arrive at relatively simple models for the seven price series, which lead to forecasts of high accuracy and stabilities with relative root mean square errors below 0.85%. Our results could serve as standalone technical forecasts. They could be combined with other forecasts for forming perspectives of price trends and carrying out policy analysis.

“…Data used for analysis are gathered from the China Real Estate Index System (CREIS). It is an analytical platform constructed to reflect market conditions and development trends of housing markets in major cities in China (Xu and Zhang, 2022k, 2023f). It was originated in 1994, which was initiated by the Development Research Center of the State Council, Real Estate Association and National Real Estate Development Group Corporation (Xu and Zhang, 2022m).…”

Section: Datamentioning

confidence: 99%

Office property price index forecasting using neural networks

2023

JFMPC

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Purpose The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021. Design/methodology/approach The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios. Findings The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases. Originality/value The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.