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The Journal of Real Estate Finance and Economics

Erschienen in:

10.11.2021

Value of Communication and Social Media: An Equilibrium Theory of Messaging

verfasst von: Paul M. Anglin, Yanmin Gao

Erschienen in: The Journal of Real Estate Finance and Economics | Ausgabe 4/2023

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Abstract

Many papers use numerical information to estimate the price of a property even if improvements in information technology enable sellers to convey more information, in an increasing variety of formats. In our model, each seller sends a message and buyers search for a good match in terms of hidden differentiators. Since the meaning of a message is determined endogenously, multiple steady state equilibria exist. A maximal equilibrium displays assortative matching and its messaging strategies maximize the flow of surplus value. We show that it exists if the matching rate is high enough. This analysis reveals differences associated with newer media. For example, video can display information in such detail that a buyer may not need to inspect the house. The real time interactivity of social media also makes better messaging possible, if not necessarily consistent with an equilibrium. The goal of many social media messages is to “go viral” and that goal requires the participation of influencers, whose motives need to be considered more carefully. We offer a number of empirical predictions, conjectures and interesting special cases. We show that if the set of possible messages is a continuum then perfect messages are possible and selling prices would adapt in a way that minimizes time on market. In a maximal equilibrium, the messaging strategy used by a given type of seller need not vary across segments if the differences between segments are observable. Since so many people think that many property descriptions are colorful exaggerations, our equilibrium model provides a context for discussing whether a message misleads.

Vorheriger Artikel Improving Mortgage Default Collection Efforts by Employing the Decoy Effect

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For advice intended for practitioners, consider https://www.zillow.com/blog/15-words-that-add-value-171182/, https://www.zillow.com/agent-resources/blog/crafting-listing-descriptions/, https://www.inman.com/2012/07/23/4-tips-writing-powerful-real-estate-copy/, https://www.zillow.com/agent-resources/blog/writing-real-estate-descriptions/, or https://www.trulia.com/blog/how-to-write-home-listing-tips/.

Other applications of messaging include online dating (e.g., Herrenbrueck et al., 2018; Hitsch et al., 2010) and search in the labor market (e.g., Brenčič, 2014; Kroft & Pope, 2014).

For example and in addition to whatever Google offers (https://cloud.google.com/natural-language/), there is txttool and ngram using Stata (https://www.stata-journal.com/article.html?article=dm0077; Schonlau et al., 2017) and Text Miner using SAS (https://www.sas.com/en_ca/software/text-miner.html). Users of R or Python have developed several relevant packages. Gentzkow et al., (2019, p. 535) “discuss the features that make text different from other forms of data [and] offer a practical overview of relevant statistical methods”. Price and Smith (2019) published their tools at https://github.com/browak/Nowak-Price-Smith-JREFE-2019. The machine-learning tools used in Johnson et al. (2020) to study the effects of “curb appeal” can be found at https://github.com/erikbjohn/curb_appeal/.

Some models of cheap talk include competition. For example, Menzio (2007) considers a labor market with identical workers and more productive employers use different messages. In the equilibrium to that model, different messages are associated with a different size of the applicant pool so that the desire to work at the high-paying- high-productivity employers is rationed by waiting time. Kim and Kircher (2015) also study how cheap talk and competition interact. They show that, in some cases, cheap talk can produce an efficient outcome. Unfortunately, they acknowledge that their result is sensitive to the type of price mechanism because even a small change from a first price auction to a second price auction is enough to disrupt their key result. Our model relies on a bargaining price mechanism which is simple, familiar and common to many housing markets.

While it is not easy to fake a photograph, continuing improvements in technology have led to concerns about “Deepfake” videos in which it is nearly impossible to detect manipulation.

The most important feature of U(.) may be that it displays a “single crossing property”, when 1 > a ≥ 0, that enables buyers to be ranked and that each buyer knows their ranking relative to other buyers. Anglin (1992) demonstrates how, under certain conditions, a general model of buyer preferences over a differentiated good can be converted into a similar expression.

The assumption of equal bargaining power is restrictive, but its familiarity allows us to emphasize other features of our model. Empirical work by Harding et al. (2003), or more recently by Hayunga and Munneke (2021) and Cohen and Harding (2020), studies this issue in more detail.

A more realistic model would recognize that most property owners use the advice of an agent when selling. A simple agency model based on hidden action implies that the objective function which rationalizes the selling strategy would combine the preferences of the seller with the abilities and incentives facing the agent. Therefore, a more relevant objective function would also recognize the effects of the prevailing commission rate, agent’s cost of effort and some risk aversion parameters. Buyers may use the advice of agents also, when decoding messages, but the relationship between a buyer and the agents requires understanding the interplay between contractual relationships and informal common practices. A fuller discussion of whether the interests of a principal and an agent are aligned is beyond the scope of this paper.

We use a familiar reservation criterion to simplify one component of our model. Many others have studied how an optimal stopping rule varies with the situation, including how the criterion might vary over time (e.g., Salant, 1991; Morgan & Manning, 1985; Zuckerman, 1984).

This normalization is not without some loss of generality: restricting the message set in this way rules out an equilibrium where some sellers send the message “I have an average house” to attract buyers who are in the middle of the distribution of buyer types.

Intuitive explanations of pricing tend to focus on “bargaining power” and “bargaining position”. A buyer, or seller, who can see more matches in less time for any reason has a stronger threat to negotiate with somebody else: their bargaining position improves. An increase in β would improve bargaining positions on both sides of the market since if every buyer sees more sellers then it must also be true that every seller sees more buyers.

There is a technicality that, if buyer type τ_u is indifferent between messages when only a seller of type q = 1 sends message G then they may randomize arbitrarily including, but not necessarily, equal probabilities. While this choice would not impose a cost of a buyer, players on the other side can be disappointed. Fortunately, the specific tie breaking rule does not affect the existence of a maximal equilibrium so long as some probability is attached to each alternative. A change in the tie breaking probability would affect the rate at which a match is made, but as β approaches infinity, the cost of time becomes unimportant: the critical lower bound on β may vary with the tie breaking rule but not the existence result.

Interested readers are encouraged to consider more complex comparative statics experiments. For example, one could investigate the effect of distorting the distribution of buyer types by mixing N^B(τ) with a Uniform distribution. More formally, for τ_u ≥ τ^∗ > τ_l, one could consider the effects of replacing N^B(τ) by N^B∗(τ) = (1 − ν)N^B(τ) + ν(τ − τ_l)/(τ^∗− τ_l) if τ^∗≥ τ ≥ τ_l, = (1 − ν)N^B(τ) + ν if τ_u ≥ τ ≥ τ^∗, = 1 if τ > τ_u and = 0 if τ < τ_l for 1 ≥ ν ≥ 0. The degree of distortion varies with ν.

An advantage of the proposed distortion is that, for τ^∗ close enough to τ_l, τ^∗ would be far from \(\hat {\tau }\). If so then the clear and direct effect of this distortion would be to increase the number of buyers looking for message H for a given \(\hat {\tau }\), with little effect on matches between seller types with q close to 1 and buyer types with τ close to τ_u. Therefore the dominant effect of this distortion is likely to be what is needed to reestablish the equality required by Proposition 4. In many cases, the analysis would require restrictive assumptions on exogenous parameters which cannot be measured directly. Therefore, we do not consider such experiments here.

The comparative statics discussion below emphasizes the effects of local variation since, if an equilibrium is not unique, a change in a parameter may also lead buyers and sellers to coordinate on a different kind of equilibrium.

We ignore technological changes which create distinctions without a difference, such as replacing paper brochures sent to home owners in a neighborhood by a website with the same information. In both cases, information is seen by a buyer only if the buyer initiates: the new media differs because a link can be embedded to that website which enables an instantaneous connection.

This comment assumes that the seller sends only one message. A seller, or their agent, could use more resources to distribute an ordinarily informative message using normal media and create a second message that might go viral. If that second message fails then the first message would have its usual effect.

Interested readers are encouraged to read the reviews by Jacobsen et al. (2018) or Piovesan and Zettler (2019, and the associated special issue) on lying. Their reviews are too broad to be summarized here. They note that not all of the incentives are financial and that some tendencies by demographic characteristics or by situation seem evident. Abeler et al. (2019) consider the preferences for truth-telling and argue that, in reality, telling a lie is costly for a typical person.

Article 1 of the NAR’s Code of Ethics https://www.nar.realtor/about-nar/governing-documents/code-of-ethics/2019-code-of-ethics-standards-of-practice

Luchtenberg et al. (2019) offer a model of reputation signaling with more specifics on the costs.

An exception to the claim that “everybody does it” may be found in cases where actions are not the result of motives alone. It would be interesting to compare the experience of properties sold using more or less experienced agents vs. sellers who use a “for sale by owner” strategy vs. certain types of sellers (such as banks) for whom motives are constrained by legal requirements. Or, in the case of commercial properties, it would be interesting to study whether verifiable financial facts dominate decision making to an extent that descriptions have little effect.

We note that the findings of Liu et al. (2020) should also encourage research in this direction because, in their report on the “tokens” used to create the indicator variables included in the empirical analysis, one word tokens tend to focus on nouns while tokens including two or more words use adjectives, or qualifiers concerning location.

It is also true that a few sellers choose to reveal problems with a property (e.g., Haag et al., 2000). While they found a correlation with the selling price, their use of ordinary least squares and transaction data implies that they cannot determine what the price would have been if a different message had been used. It is possible that using a different message would have attracted an inappropriate segment of consumers and would have produced an even lower expected selling price and a longer expected time on market.

Allen and Carter (2010) and Haag et al. (2000) report that a message such as “below market value” or “good buy” is used by about one percent of sellers. They show that the message is associated with a lower price but, because the time on market does not differ significantly, the message does not appear to attract unusual numbers of buyers. While the list price plays no role in our model, because all sellers are assumed to have the same cost of time, we note that neither of these papers investigate whether the message is consistent with the choice of list price or an overpricing strategy. Also, neither paper investigates whether the description is used more frequently in a buyers’ market or whether the effects are smaller when market conditions make bidding wars more common.

We are indebted to an anonymous referee for this suggestion. Formally, our argument focuses on differences in the ratio of buyers to sellers even if some people classify an imbalance according to price trends. Intuition and many different kinds of models tell economists that excess demand causes prices to rise but, to build on the ideas discussed at the end of Section “Characterizing the Matches in the Limit”, solving for the average price level in our model requires knowing how the inflows of buyers and sellers vary with prices.

If expectations of buyers and sellers are incompatible, but there are gains from trade, then any theory faces a challenging question: why doesn’t a buyer or seller alter their bid or offer? Equilibrium theories have trouble answering this question because buyers and sellers mistake a rational decision based on an inaccurate inference with an irrational decision based on an accurate inference. A better answer would start with a pricing mechanism which is less sensitive to differences of opinion about market conditions, but that discussion is beyond the scope of this paper.

This discussion focuses on the case of the limiting value of β because that case enables us to compare q and h(q). If β is large enough then ψ_l(q) and ψ_u(q) are close enough to h(q) for the logic to be valid. For smaller values of β, where h(q) differs from ψ_l(q) and ψ_u(q) by too much, a deeper discussion of the linkage between the unbalanced market conditions, the matching process and the distributions of buyer and seller types may be required.

We choose these messages to emphasize the idea that our model focuses on information about q. We ignore questions which focus on dimensions that would be important to some but not all buyers, such as the quality of a new kitchen or landscaped garden.

Even if our model distinguishes G⁺ and H⁺, the text of seller’s response may be the same: e.g., the text of both G⁺ and H⁺ could be “Yes” and a buyer would understand the difference because the message is a response to a specific question. We assume that a seller who sends message G initially and responds with the text in message H⁻, for example, would confuse a buyer so much that the buyer ignores that seller.

See https://www.zillow.com/agent-resources/blog/real-estate-hashtags/, or https://www.nar.realtor/sites/default/fies/documents/2018-real-estate-in-a-digital-world-12-12-2018.pdffor examples of advice for practitioners.

These results use uniform convergence since an increase in β decreases the cost of search for every individual equally. Therefore, for any 𝜖 there is a \(\tilde {\beta }\) such that, for all q ∈ [0, 1], |ψ_u(q) − h(q)| < 𝜖 and |ψ_l(q) − h(q)| < 𝜖 if \(\beta > \tilde {\beta }\).

Abeler, J., Nosenzo, D., & Raymond, C. (2019). Preferences for truth-telling. Econometrica, 87(2), 1115–1153. https://www.econometricsociety.org/publications/econometrica/2019/02/01/preferences-truth-telling.CrossRef

Akpinar, E., & Berger, J. (2017). Valuable virality. Journal of Marketing Research, 54, 318–330. https://journals.sagepub.com/doi/10.1509/jmr.13.0350.CrossRef

Allen, M., & Carter, C. (2010). Do the phrases “Below Market Value” or “Below Appraised Value” in MLS listings convey useful information to the market?. Journal of Housing Research, 19(2), 185–194. https://www.aresjournals.org/doi/abs/10.5555/jhor.19.2.b3703xv248360351.CrossRef

Allen, M.T., Cadena, A., Rutherford, J., & Rutherford, R.C. (2015). Effects of real estate brokers’ marketing strategies: Public open houses, broker open Houses, MLS virtual tours, and MLS photographs. Journal of Real Estate Research, 37(3), 343–369. https://aresjournals.org/doi/abs/10.5555/0896-5803.37.3.343.CrossRef

Anderson, S.J., Dewhirst, T., & Ling, P.M. (2006). Every document and picture tells a story: Using internal corporate document reviews, semiotics, and content analysis to assess tobacco advertising. Tobacco Control, 15, 254–261. https://tobaccocontrol.bmj.com/content/15/3/254.CrossRef

Anglin, P.M. (1992). The relationship between models of horizontal and vertical differentiation. Bulletin of Economic Research, 44(1), 1–20. https://doi.org/10.1111/j.1467-8586.1992.tb00503.x.CrossRef

Babić Rosario, A., de Valck, K., & Sotgiu, F. (2020). Conceptualizing the electronic word-of-mouth process: What we know and need to know about eWOM creation, exposure, and evaluation. Journal of the Academy of Marketing Science, 48(3), 422–448. https://doi.org/10.1007/s11747-019-00706-1.CrossRef

Bailey, M, Cao, R., Kuchler, T., & Stroebel, J. (2018). The economic effects of social networks: Evidence from the housing market. Journal of Political Economy, 126(6), 2224–2276. https://www.journals.uchicago.edu/doi/full/10.1086/700073.CrossRef

Benefield, J.D., Cain, C.L., & Johnson, K.H. (2011). On the relationship between property price, time-on-market, and photo depictions in a multiple listing service. Journal of Real Estate Finance and Economics, 43, 401–422. https://doi.org/10.1007/s11146-009-9219-6.CrossRef

Beracha, E., Lang, M., & Hausler, J. (2019). On the relationship between market sentiment and commercial real estate performance–A textual analysis examination. Journal of Real Estate Research, 41(4), 605–638. https://www.tandfonline.com/doi/abs/10.22300/0896-5803.41.4.605.CrossRef

Berger, J., & Milkman, K. (2012). What makes online content viral?. Journal of Marketing Research, 49, 192–205. https://journals.sagepub.com/doi/abs/10.1509/jmr.10.0353.CrossRef

Bijerk, S., Karamychev, V., & Swank, O. (2018). When words are not enough. Journal of Bahavior and Organization, 149, 294–314. https://doi.org/10.1016/j.jebo.2018.03.009.

Brenčič, V. (2014). Search online: Evidence from acquisition of information on online job boards and resume banks. Journal of Economic Psychology, 42, 112–125. https://doi.org/10.1016/j.joep.2014.02.003.CrossRef

Carroll, G., & Egorov, G. (2019). Strategic communication with minimal verification. Econometrica, 87(6), 1867–1892. https://doi.org/10.3982/ECTA15712.CrossRef

Chung, C.K., & Pennebaker, J.W. (2018). Textual analysis. Measurement in Social Psychology 153–173 Routledge. https://psycnet.apa.org/record/2019-06891-007.

Cohen, J.P., & Harding, J.P. (2020). The bargaining and contagion effects of investors in single family residential properties: The case of Denver Colorado. Journal of Real Estate Finance and Economics. https://link.springer.com/article/10.1007/s11146-020-09766-5.

Crawford, V.P., & Sobel, J. (1982). Strategic information transmission. Econometrica, 50, 1431–1451. https://www.jstor.org/stable/1913390.CrossRef

Das, P., Ziobrowski, A., & Coulson, N.E. (2015). Online information search, market fundamentals and apartment real estate. Journal of Real Estate Finance and Economics, 51(4), 480–502. https://doi.org/10.1007/s11146-015-9496-1.CrossRef

DuBois, D., Bonezzi, A., & De Angelis, M. (2016). Sharing with friends vs. strangers: How interpersonal closeness influences word-of-mouth valence. Journal of Marketing Research, 53, 712–727. https://journals.sagepub.com/doi/10.1509/jmr.13.0312.CrossRef

Gentzkow, M., Kelly, B., & Taddy, M. (2019). Text as data. Journal of Economic Literature, 57(3), 535–74. https://www.aeaweb.org/articles?id=10.1257/jel.20181020.CrossRef

Ginsburgh, V., & Weber, S. (2020). The economics of language. Journal of Economic Literature, 58(2), 348–404. https://doi.org/10.1257/jel.20191316.CrossRef

Goel, S., Anderson, A., Hofman, J., & Watts, D.J. (2016). The structural virality of online diffusion. Management Science, 62(1), 180–196. https://doi.org/10.1287/mnsc.2015.2158.CrossRef

Goodwin, K., Waller, B., & Weeks, H.S. (2014). The impact of broker vernacular in residential real estate. Journal of Housing Research, 23 (2), 143–162. https://aresjournals.org/doi/abs/10.5555/jhor.23.2.b3456l03802v4854.CrossRef

Goodwin, K., Waller, B., & Weeks, H.S. (2018). Connotation and textual analysis in real estate listings. Journal of Housing Research, 27(2), 93–106. https://doi.org/10.1080/10835547.2014.12092089.CrossRef

Haag, J., Rutherford, R., & Thomson, T. (2000). Real estate agent remarks: Help or hype?. Journal of Real Estate Research, 20(1-2), 205–215. https://aresjournals.org/doi/abs/10.5555/rees.20.1-2.20370163666t126l.CrossRef

Hagenbach, J., Koessler, F., & Trégouët, T. (2017). Truthtelling in matching markets. Scandinavian Journal of Economics, 119, 882–909. https://onlinelibrary.wiley.com/doi/full/10.1111/sjoe.12203.CrossRef

Han, L., & Strange, W.C. (2015). The microstructure of housing markets: Search, bargaining, and brokerage. In G. Duranton, V.J. Henderson, & W. Strange (Eds.) Handbook of Regional and Urban Economics. https://doi.org/10.1016/B978-0-444-59531-7.00013-2, Vol. 5.

Harding, J.P., Rosenthal, S.S., & Sirmans, C.F. (2003). Estimating bargaining power in the market for existing homes. Review of Economics and Statistics, 85(1), 178–188. https://www.jstor.org/stable/3211631.CrossRef

Haurin, D. (1988). The duration of marketing time of residential housing. Real Estate Economics, 16, 396–410. https://doi.org/10.1111/1540-6229.00463.CrossRef

Hayunga, D.K., & Munneke, H.J. (2021). Examining both sides of the transaction: Bargaining in the housing market. Real Estate Economics. https://doi.org/10.1111/1540-6229.12272.

Herrenbrueck, L., Xia, X., Eastwick, P., & Hui, C.M. (2018). Smart-dating in speed-dating: How a simple search model can explain matching decisions, (Vol. 106. https://www.sciencedirect.com/science/article/pii/S0014292118300606.

Hitsch, G.J., Hortaçsu, A., & Ariely, D. (2010). Matching and sorting in online dating. American Economic Review, 100(1), 130–63. http://www.aeaweb.org/articles?id=10.1257/aer.100.1.130.CrossRef

Humphrey, A., & Wang, R.J.-W. (2018). Automated text analysis for consumer research. Journal of Consumer Research, 44, 1274–1306. https://doi.org/10.1093/jcr/ucx104.CrossRef

Jacobsen, C., Fosgaard, T.R., & Pascual-Ezama, D. (2018). Why do we lie? a practical guide to the dishonesty literature. Journal of Economic Surveys, 32(2), 357–387. https://doi.org/10.1111/joes.12204.CrossRef

Johnson. (2013). Do different languages confer different personalities? Economist, Nov. 5, 2013. https://www.economist.com/prospero/2013/11/05/johnson-do-different-languages-confer-different-personalities.

Johnson, E.B., Tidwell, A., & Villupuram, S.V. (2020). Valuing curb appeal. Journal of Real Estate Finance and Economics, 60, 111–133. https://doi.org/10.1007/s11146-019-09713-z.CrossRef

Ke, Z., Kelly, B.T., & Xiu, D. (2020). Predicting returns with text data. University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2019-69, Yale ICF Working Paper No. 2019-10, Chicago Booth Research Paper No. 20-37, Available at SSRN: https://ssrn.com/abstract=3389884.

Kim, K., & Kircher, P. (2015). Efficient competition through cheap talk: The case of competing auctions. Econometrica, 83, 1849–1875. https://doi.org/10.3982/ECTA12103.CrossRef

Kroft, K., & Pope, D. (2014). Does online search crowd out traditional search and improve matching efficiency? Evidence from Craigslist. Journal of Labor Economics, 32(2), 259–303. http://www.journals.uchicago.edu/doi/abs/10.1086/673374.CrossRef

Lane, M., Seiler, M., & Seiler, V. (2015). The impact of staging conditions on residential real estate demand. Journal of Housing Research, 24(1), 21–36. https://aresjournals.org/doi/abs/10.5555/1052-7001.24.1.21.CrossRef

Lee, D., Hosanager, K., & Nair, H. (2018). Advertising content and consumer engagement on social media. Management Science, 64(11), 5105–5131. https://doi.org/10.1287/mnsc.2017.2902.CrossRef

Levitt, S., & Syverson, C. (2008). Market distortions When agents Are better informed: The value of information in real estate transactions. Review of Economics and Statistics, 90(4), 599–611. https://doi.org/10.1162/rest.90.4.599.CrossRef

Li, F. (2010). Textual analysis of corporate disclosures: a survey of the literature. Journal of Accounting Literature, 29, 143–165. https://doi.org/10.1111/1475-679X.12123.

Li, Y., & Xie, Y. (2020). Is a picture worth a thousand words? an empirical study of image content and social media engagement. Journal of Marketing Research, 57(1), 1–19. https://doi.org/10.1177/0022243719881113.CrossRef

Liu, C.H., Nowak, A.D, & Smith, P.S. (2020). Asymmetric or incomplete information about asset values?. Review of Financial Studies, 33(7), 2898—2936. https://doi.org/10.1093/rfs/hhz096.CrossRef

Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. Journal of Finance, 66, 35–65. https://www.jstor.org/stable/29789771.CrossRef

Luchtenberg, K.F., Seiler, M.J., & Sun, H. (2019). Listing agent signals: Does a picture paint a thousand words?. Journal of Real Estate Finance and Economics, 59, 617–648. https://doi.org/10.1007/s11146-018-9674-z.CrossRef

Martin, S., Saalfeld, T., Strøm, K., Slapin, J., & Proksch, S. (2014). Words as data: Content analysis in legislative studies. in The Oxford Handbook of Legislative Studies. Oxford: Oxford University Press. https://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780199653010.001.0001/oxfordhb-9780199653010-e-033.CrossRef

Menzio, G. (2007). A theory of partially directed search. Journal of Political Economy, 115, 748–769. https://www.journals.uchicago.edu/doi/abs/10.1086/525544.CrossRef

Meshcheryakov, A. (2018). Using online search queries in real estate research with an empirical example of arson forecast. Journal of Real Estate Literature, 26(2), 331–361. https://aresjournals.org/doi/abs/10.5555/0927-7544.26.2.331.CrossRef

Morgan, P., & Manning, R. (1985). Optimal search. Econometrica, 53(4), 923–944. https://www.jstor.org/stable/1912661.CrossRef

Mullainathan, S., & Spiess, J (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87–106. http://www.jstor.org/stable/44235000.CrossRef

Price, B.S., & Smith, P.S. (2019). Real estate dictionaries across space and time, Journal of Real Estate Finance and Economics, forthcoming. https://link.springer.com/article/10.1007/s11146-019-09740-w.

Nowak, A., & Smith, P. (2017). Textual analysis in real estate. Journal of Applied Econometrics, 32, 896–918. https://doi.org/10.1002/jae.2550.CrossRef

Piazzesi, M., Schneider, M., & Stroebel, J. (2020). Segmented housing search. American Economic Review, 110(3), 720–759. https://doi.org/10.1257/aer.20141772.CrossRef

Piovesan, M., & Zettler, I. (2019). Editorial: Uncovering dishonesty. Journal of Economic Psychology, 71, 1–3. https://doi.org/10.1016/j.joep.2019.03.003.CrossRef

Rothschild, M., & Stiglitz, J. (1976). Equilibrium in competitive insurance markets: an essay on the economics of imperfect information. Quarterly Journal of Economics, 90(4), 629–649. https://doi.org/10.2307/1885326.CrossRef

Ruscheinsky, J.R., Lang, M., Dietzel, M.A., & Schaefers, W. (2018). Creating a real estate sentiment index through textual analysis of internet data. working paper, University of Regensburg.

Salant, S.W (1991). For sale by owner: When to use a broker and how to price the house. Journal of Real Estate Finance and Economics, 4(2), 157–173. https://link.springer.com/article/10.1007/BF00173122.CrossRef

Salzman, D., & Zwinkels, R.C.J. (2017). Behavioral real estate. Journal of Real Estate Literature, 25(1), 77–106. https://aresjournals.org/doi/abs/10.5555/0927-7544.25.1.77.CrossRef

Schonlau, M., Guenther, N., & Sucholutsky, I (2017). Text mining with n-gram variables. Stata Journal, 17(4), 866–881. Retrieved from https://www.stata-journal.com/article.html?article=st0502.CrossRef

Schulze, C., Scholer, L., & Skiera, B. (2014). Not all fun and games: Viral marketing for utilitarian products. Journal of Marketing, 78, 1–19. https://journals.sagepub.com/doi/10.1509/jm.11.0528.CrossRef

Shen, L., & Ross, S. (2021). Information value of property description: a machine learning approach. Journal of Urban Economics 121. https://doi.org/10.1016/j.jue.2020.103299.

Shimer, R., & Smith, L. (2000). Assortative matching and search. Econometrica, 68(2), 343–369. https://doi.org/10.1111/1468-0262.00112.CrossRef

Smith, L. (2006). The marriage model with search frictions. Journal of Political Economy, 114, 1124–1144. https://www.journals.uchicago.edu/doi/abs/10.1086/510440.CrossRef

Sobel, J. (2020). Lying and deception in games. Journal of Political Economy, 128(3), 907–947. https://www.journals.uchicago.edu/doi/abs/10.1086/704754.CrossRef

Spence, M. (1978). Job market signaling. In P. Diamond M. Rothschild (Eds.) Uncertainty in Economics. https://doi.org/10.1016/B978-0-12-214850-7.50025-5(pp. 281–306). Academic Press.

Sung, E. (2021). The effects of augmented reality mobile app advertising: Viral marketing via shared social experience. Journal of Business Research, 122, 75–87. https://www.sciencedirect.com/science/article/abs/pii/S0148296320305439.CrossRef

Tellis, G.J., MacInnis, D.J., Tirunillai, S., & Zhang, Y. (2019). What drives virality (sharing) of online digital content? The critical role of information, emotion, and brand prominence. Journal of Marketing, 83(4), 1–20. https://journals.sagepub.com/doi/10.1177/0022242919841034.CrossRef

van Dijk, D.W., & Francke, M.K. (2018). Internet search behavior, liquidity and prices in the housing market. Real Estate Economics, 46, 368–403. https://onlinelibrary.wiley.com/doi/10.1111/1540-6229.12187.CrossRef

Winson-Geideman, K. (2018). Sentiments and semantics: A review of the content analysis literature in the era of big data. Journal of Real Estate Literature, 26(1), 1–12. https://aresjournals.org/doi/abs/10.5555/0927-7544.26.1.1.CrossRef

Wright, R., Kircher, P., Julien, B., & Guerrieri, V. (2021). Directed search and competitive search equilibrium: a guided tour. Journal of Economic Literature, 59(1), 90–148. https://doi.org/10.1257/jel.20191505.CrossRef

Zuckerman, D. (1984). On preserving the reservation wage property in a continuous job search model. Journal of Economic Theory, 34, 175–179. https://www.sciencedirect.com/science/article/abs/pii/0022053184901698.CrossRef

Titel: Value of Communication and Social Media: An Equilibrium Theory of Messaging
verfasst von: Paul M. Anglin
Yanmin Gao
Publikationsdatum: 10.11.2021
Verlag: Springer US
Erschienen in: The Journal of Real Estate Finance and Economics / Ausgabe 4/2023
Print ISSN: 0895-5638
Elektronische ISSN: 1573-045X
DOI: https://doi.org/10.1007/s11146-021-09865-x

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