scholarly journals Measuring the Housing Market Demand Elasticity in China—Based on the Rational Price Expectation and the Provincial Panel Data

2016 ◽  
Vol 04 (01) ◽  
pp. 21-25 ◽  
Author(s):  
Mengfei Huang ◽  
Botao Lu
Keyword(s):  
Panel Data ◽  
Housing Market ◽  
Demand Elasticity ◽  
Market Demand ◽  
Price Expectation

scholarly journals A Panel Data Estimation of Domestic Water Demand with IRT Tariff Structure: The Case of the City of Valencia (Spain)

2021 ◽  
Vol 13 (3) ◽  
pp. 1414
Author(s):  
Mónica Madonado-Devis ◽  
Vicent Almenar-Llongo
Keyword(s):  
Panel Data ◽  
Water Demand ◽  
Large Data ◽  
Demand Elasticity ◽  
Average Price ◽  
Domestic Water ◽  
Domestic Water Demand ◽  
Household Level ◽  
Price Structure ◽  
The City

In urban water provisioning, prices can improve efficiency, contributing to the achievement of the environmental objective. However, household responses to price changes differ widely based on the household characteristics. Analyses performed at the aggregate level ignore the implications of water demand incentives at the individual household level. A large data sample at the household level enables estimation of econometric models of water demand, capturing the heterogeneity in domestic consumption. This study estimated the domestic water demand in the city of Valencia and its elasticity, along with the demands of its different districts and neighbourhoods (intra-urban scale analysis). Water price structure in Valencia is completely different from that of other Spanish cities: it is a price structure of increasing volume (increasing rate tariffs, IRT). For this estimation, from a microdata panel at the household level, the demand function with average prices for the period 2008–2011 was estimated using panel data techniques including a fixed effect for each neighbourhood. The domestic water demand elasticity at the average price in Valencia was estimated at −0.88 (which is higher than that estimated for other Spanish cities). This value indicates an inelastic demand at the average price of the previous period, which can cause consumers to overestimate the price and react more strongly to changes.

Market-Savvy Housing and Community Development Policy

2018 ◽  
pp. 173-196
Author(s):  
Todd Swanstrom
Keyword(s):  
Community Development ◽  
Housing Market ◽  
Housing Development ◽  
Market Demand ◽  
Housing Supply ◽  
Housing Policies ◽  
Building Market ◽  
Market Areas ◽  
Market Strategies ◽  
Middle Market

This chapter addresses how housing policies to combat segregation often fail to consider housing market strength, which varies significantly within and between metropolitan areas. In weak housing markets, policymakers should not focus on increasing the housing supply but on building market demand. This is very difficult in the weakest markets, and it often makes more sense to encourage residents to move to better neighborhoods. Housing development funds should be concentrated on “middle neighborhoods”—areas where the market is still healthy but there is a threat of decline. Middle market strategies to improve market confidence can be highly effective. By contrast, it makes no sense to invest scarce development funds in strong market neighborhoods. Instead, cities should help low-income households to stay or move into these high-opportunity areas. The chapter concludes by arguing that policymakers who embrace the build-on-strength approach advocated here need to take into account how racist practices have unfairly trapped minorities in weak market areas.

scholarly journals International Real Estate Review

2017 ◽  
Vol 20 (3) ◽  
pp. 375-396
Author(s):  
Gary Wai Chung Wong ◽  
◽  
Lok Sang Ho ◽  
Keyword(s):  
Interest Rate ◽  
Housing Market ◽  
Supply And Demand ◽  
Housing Prices ◽  
Housing Price ◽  
Price Expectations ◽  
Price Expectation ◽  
Long Run ◽  
Property Price ◽  
Explicit Consideration

This paper builds on the literature that shows policy often plays a key role in housing cycles. Using the cointegration approach which focuses on the supply and demand dynamics of the housing market, and with explicit consideration of housing price expectations proxied by the price-earning ratio in financial markets, this paper identifies two cointegrating relations: a long run demand-side relation that involves housing property price, interest rate, price expectation and income; and a supply-side relation that involves private housing completion, property price, interest rate, and building and land costs. Based on Hong Kong data from 1990 a£á¡§ 2012, which covers big cycles in the housing market, this paper suggests that policies to augment or restrain housing supply in the attempt to stabilize housing prices have been counterproductive.

Financial engineering in pricing agricultural derivatives based on demand and volatility

2016 ◽  
Vol 76 (1) ◽  
pp. 42-53 ◽  
Author(s):  
Hirbod Assa
Keyword(s):  
Financial Engineering ◽  
Commodity Prices ◽  
Demand Elasticity ◽  
Insurance Companies ◽  
Leverage Effect ◽  
Model Parameters ◽  
Market Demand ◽  
Agricultural Commodities ◽  
Cev Model ◽  
Content Type

Purpose – The purpose of this paper is twofold. First, the author proposes a financial engineering framework to model commodity prices based on market demand processes and demand functions. This framework explains the relation between demand, volatility and the leverage effect of commodities. It is also shown how the proposed framework can be used to price derivatives on commodity prices. Second, the author estimates the model parameters for agricultural commodities and discuss the implications of the results on derivative prices. In particular, the author see how leverage effect (or inverse leverage effect) is related to market demand. Design/methodology/approach – This paper uses a power demand function along with the Cox, Ingersoll and Ross mean-reverting process to find the price process of commodities. Then by using the Ito theorem the constant elastic volatility (CEV) model is derived for the market prices. The partial differential equation that the dynamics of derivative prices satisfy is found and, by the Feynman-Kac theorem, the market derivative prices are provided within a Monte-Carlo simulation framework. Finally, by using a maximum likelihood estimator, the parameters of the CEV model for the agricultural commodity prices are found. Findings – The results of this paper show that derivative prices on commodities are heavily affected by the elasticity of volatility and, consequently, by market demand elasticity. The empirical results show that different groups of agricultural commodities have different values of demand and volatility elasticity. Practical implications – The results of this paper can be used by practitioners to price derivatives on commodity prices and by insurance companies to better price insurance contracts. As in many countries agricultural insurances are subsidised by the government, the results of this paper are useful for setting more efficient policies. Originality/value – Approaches that use the methodology of financial engineering to model agricultural prices and compute the derivative prices are rather new within the literature and still need to be developed for further applications.

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