Following the Market? Hedonic Farmland Valuation Using Sales Prices versus Self-reported Values

Bigelow, Daniel P.; Ifft, Jennifer; Kuethe, Todd

doi:10.3368/le.96.3.418

Cited by 26 publications

(17 citation statements)

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“…However, the selection bias is not responsible for the result that the estimated implicit prices differ between the two data sets. Genesove and Mayer (2001) and Bigelow et al (2020) provide explanations for this result. Sellers (and agents) can misperceive the value to others of characteristics that are salient to them (the price they paid, the importance of amenities).…”

Section: Discussionmentioning

confidence: 77%

“…Genesove and Mayer ( 2001 ) and Bigelow et al. ( 2020 ) provide explanations for this result. Sellers (and agents) can misperceive the value to others of characteristics that are salient to them (the price they paid, the importance of amenities).…”

Section: Discussionmentioning

confidence: 90%

“…Banzhaf and Farooque (2013) find that self-assessed values are less useful in hedonic analysis than transaction prices. The recent paper by Bigelow et al (2020) contains an excellent summary of this literature. Their study compares hedonic prices estimated from selfassessed values and market transactions and finds that owners misjudge the value of land characteristics that are salient to them.…”

Section: Introductionmentioning

confidence: 99%

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Real estate listings and their usefulness for hedonic regressions

et al. 2021

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Real estate platforms provide a new source of data which has already been used as a substitute for transaction data in hedonic regression applications. This paper asks whether it is valid to do so in the established research areas of (1) willingness to pay estimation, (2) automated valuations, and (3) price index construction. It therefore compares listings and transaction data and regression results derived from them. We find that ask prices stochastically dominate sale prices, mainly because the composition of characteristics differs between the two data sets. But estimates of implicit prices also differ. As a result, willingness to pay estimates from listings data can be widely off when compared with estimates from transaction data. Listings data are not very useful to predict market values of individual houses either, as these predictions suffer from upward bias and large error variance. We find, however, that an ask price index complements a sale price index, as it is useful for nowcasting.

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Section: Discussionmentioning

confidence: 77%

Section: Discussionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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Real estate listings and their usefulness for hedonic regressions

et al. 2021

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“…There are also other factors, of a more strictly operational nature that can affect the reliability of estimates based on market prices, such as the possibility that the sales samples detected may not be representative of reality as they consist of land that is more likely to be sold (sample selection bias) (Bigelow et al, 2020). 6 Note however that in our study the variable relating to transactions between relatives was not statistically significant.…”

Section: Discussionmentioning

confidence: 59%

“…From an operational point of view, in some cases scholars have used values estimated by experts or by the farmers and not market prices (Boisvert et al, 1997;Borchers et al, 2014;Choumert and Phélinas, 2015;De Noni et al, 2019;Devadoss and Manchu, 2007;Drescher and McNamara, 1999;Lehn and Bahr, 2018;Maddison, 2000;Mela et al, 2012;Sills and Caviglia-Harris, 2009). As observed by some authors, the values derived by opinion survey tends to diverge from the market price and therefore its use cannot be considered correct for appraisal purposes (Bigelow et al, 2020;Ma and Swinton, 2012).…”

Section: Introductionmentioning

confidence: 99%

Farmland value in the “Conegliano Valdobbiadene Prosecco Superiore PGDO” area. An application of the Hedonic Pricing method.

Tempesta

Foscolo²,

Nardin

et al. 2021

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In the last 30 years, numerous studies analysed the factors that affect land prices mainly using the Hedonic Pricing method. These studies have shown that many factors can affect land prices (e.g. land and surrounding territory characteristics, accessibility, proximity to urban area, etc.). However, they rarely addressed the analysis of the reliability of the models by comparing the estimated values to the observed one. Attempting to face this problem, our study analysed the land market of the “Conegliano Valdobbiadene Prosecco Superiore PGDO” area. Despite the quite high coefficient of determination (r2 = 0.76) and statistical significance of the model parameters, we found that the percentage absolute deviation between observed and estimated value is higher than 30% in 34% of cases. Our results seem to suggest that future researches should devote particular attention to the analysis of the discrepancies existing between estimated values and market prices in order to support the appraisal activity of professional valuers.

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The economics of property rights in digital farming data: Implications for farmland markets

DeLay

Boehlje

Ferrell

2023

Applied Eco Perspectives Pol

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The emergence of precision farming technologies has increased the amount and detail of farming data collected by producers. Data increases farm profitability by complementing digitally connected equipment and improving on‐farm decision making. The value generated by farm data may be capitalized into the underlying farmland asset, potentially raising sale and rental prices. However, the absence of clear property rights over farmland and farm operating practice data limits the ability to capture this value. We explore the issue of farm data through a property rights and transaction costs lens, and propose a conceptual framework of farm data valuation to identify conditions under which landowners and tenant‐operators can engage in mutually beneficial negotiation over farm data. We conclude that establishing property rights to farm data within the farmland lease can facilitate welfare‐improving exchange, allowing farm data records to be allocated to their highest valued use.

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Following the Market? Hedonic Farmland Valuation Using Sales Prices versus Self-reported Values

Cited by 26 publications

References 0 publications

Real estate listings and their usefulness for hedonic regressions

Real estate listings and their usefulness for hedonic regressions

Farmland value in the “Conegliano Valdobbiadene Prosecco Superiore PGDO” area. An application of the Hedonic Pricing method.

The economics of property rights in digital farming data: Implications for farmland markets

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