1# WP19 — Literature review and novelty audit (Phase 1)23Date: 2026-10-20. Basis: 132 references verified against Crossref (129 with a DOI resolved; 3 with a publisher/RePEc evidence4file), merged from two independent searches (`lit_part_A.csv`, theory, 73 rows; `lit_part_B.csv`, evidence and measurement, 665rows; 7 papers in both; 4 duplicate keys reconciled, see `literature_merge_report.md`). Raw Crossref records are in6`verification/`. Unverified or deliberately excluded candidates are listed in `lit_unverified_A.md` and `lit_unverified_B.md`7(notably: no formal theory of financing contingencies beyond `bian2018bargaining`; no verified Québec or Canadian time-on-market8study). Detailed thematic notes: `lit_notes_A.md`, `lit_notes_B.md`. Model-design implications are collected in §8.910## 1. Housing search theory: from vacancies to liquidity1112The matching approach to housing starts with `arnott1989housing` (idiosyncratic tastes generate equilibrium vacancies) and13`wheaton1990vacancy` (a mismatched owner searches while carrying a vacant unit; the Nash-bargained price depends on the vacancy14rate through expected time to sell and the cost of holding two houses). `yavas1992simple` adds two-sided search intensity;15`williams1995pricing` prices heterogeneous real assets as optimal-stopping values under Poisson buyer arrivals, giving the16first explicit search-induced discount. `krainer2001theory` is the reference theory of residential *liquidity*: buyers draw17match values, sellers set reservation prices anticipating the aggregate state, and time on market lengthens in downturns because18waiting is rational; `krainer2002equilibrium` values an illiquid asset relative to a liquid benchmark as a function of arrival19intensity. `han2015microstructure` organises the field (one-sided search, random matching, directed search, bargaining and20auctions, brokerage). WP19 is a random-matching, Nash-bargaining model with a posted-price robustness check and inherits from21this strand the two objects it formalises: the sale hazard and a discount against a frictionless benchmark.2223The important distinction for WP19 is that the "liquidity shock" of `krainer2001theory` is a shock to the dividend (housing24services), whereas WP19 studies shocks to matching efficiency and buyer qualification with the dividend distribution fixed.25`ngai2014hot` is the closest precedent for a non-fundamental driver: thick-market effects on match quality turn small seasonal26drivers into large price and volume swings. `novymarx2009hot` shows that feedback between the buyer–seller ratio and bargaining27positions amplifies demand shocks; `diaz2013house` (random matching, bargaining, entry) and `head2014search` (search with28construction and buyer entry, momentum from time-varying time to sell) reproduce the cyclical comovements; `ngai2020decision`,29`ngai2024ins`, `moen2021buying`, `gabrovski2019beveridge`, `caplin2011trading`, `guren2018house`, `anenberg2016information`,30`leung2017flipping` and `piazzesi2020segmented` complete the aggregate-dynamics branch. WP19 is stationary and local in scope: it31asks what a fall in $A$ or $\phi$ does to the *cross-section* of hazards and prices and to the composition of transactions.3233## 2. Seller urgency and heterogeneity3435The nearest antecedent of WP19's central device is `albrecht2007opportunistic`: buyers and sellers switch from a "relaxed" to a36"desperate" flow value at a Poisson rate, and the bargained price depends on the pair's states; the expected price conditional on37time on market falls with duration while its variance is hump-shaped. Other treatments of urgency are deadlines38(`salant1991sale`), the seller's discount rate (`arnold1999search`), the endogenous cost of holding two homes39(`anenberg2020endogenous`), debt overhang that *raises* asking prices (`head2023default`), and an estimated motivation parameter40(`carrillo2012empirical`). The empirical call for heterogeneous-seller theory is explicit in `glower1998motivation`: planned-move41sellers sell faster and motivation lowers the sale price but not the list markup; `springer1996motivation` finds lower prices for42eager, relocated, vacant and foreclosed sellers; `harding2003bargainingeffects` finds weaker bargaining power for vacant sellers;43`levitt2008agents` provides the marginal value of waiting (about 3.7% for about 9.5 extra days). Equity and reference dependence44work in the opposite direction: high-LTV and loss-facing sellers set higher asking prices and sell more slowly45(`genesove1997equity`, `genesove2001lossaversion`), and patient sellers sort into FSBO platforms (`hendel2009fsbo`).4647In all theoretical treatments urgency is a two-state process, a deadline, a debt position or a nuisance parameter. None models a48continuous, permanent carrying-cost type whose cross-sectional distribution determines the stock of listings, the composition of49transactions and, through free entry, market tightness. That is WP19's modelling choice. The evidence on constrained sellers50implies that the carrying-cost distribution should admit very patient types (low $c$) and that the model's predictions for51"locked" sellers are limited to the case $c\ge0$ (Limitations).5253## 3. Forced sales and the size of liquidity discounts5455`campbell2011forced` (foreclosure discount about 27% in Massachusetts, smaller discounts for death and bankruptcy sales, about561% spillover per nearby foreclosure), `clauretie2009foreclosure` (naive discounts above 20% overstated by about one third once57condition, endogenous marketing time and spatial dependence are controlled), `penningtoncross2006reo`, `mayer1998auctions` (price58of immediacy 0–9% in a boom versus 9–21% in a bust) and `andersen2017firesales` (sudden-death forced sales: discounts rising with59urgency, poor market conditions and seller constraints, with demand held fixed) establish the empirical range of the urgency60discount and its state dependence. Spillovers (`harding2009contagion`, `anenberg2014foreclosures`, `mian2015foreclosures`) show61that a larger mass of urgent sellers lowers prices for all sellers without a change in fundamentals, the empirical content of62WP19's liquidity-shock proposition. What this literature does not provide is the decomposition the model delivers: how much of63the discount is bargaining under high carrying cost and how much is selection of which houses transact.6465## 4. Credit, qualification and the cash–mortgage gap6667On the theory side, `hedlund2016illiquidity`, `hedlund2016cyclical` and `garriga2020mortgage` make search-based illiquidity a68state variable interacting with long-term defaultable mortgages; `guren2020foreclosures` show how foreclosures freeze the69non-distressed market; `eerola2018borrowing` find that a moderate credit tightening raises time on market and idiosyncratic70price dispersion; `gabrovski2021search` open a channel from credit to liquidity with multiplicity; `diaz2024housing` has wealthier71buyers paying to transact faster under competitive search; `stein1995prices` and `ortalomagne2006housing` are the72frictionless-with-credit benchmarks. On the evidence side, qualification is elastic and delays are measurable73(`anenberg2019creditavailability`, `bhutta2021insurance`, `defusco2020leverage`, `fuster2019technology`); `allen2019searchfrictions`74is the only verified Canadian structural evidence (insured mortgages as a search market).7576The cash–mortgage gap is the empirical object WP19's financing extension speaks to. `reher2024mortgage` estimate an 11% mortgage77premium (1980–2017) against about 3% from a representative-seller model and find that heterogeneity in selling conditions closes78half of the gap; `han2024cash` estimate a 2–3.9% cash discount with 29% faster closing and show, with a calibrated search model,79that closing risk alone is insufficient — the risk that a relisted property fails to sell is needed, so the discount is smaller80in booms and larger markets. `asabere1992cash`, `jauregui2019fundingprice`, `aroul2023cashdiscount`, `lin2025contingencies` and81`buchak2026ibuyers` (about 5% immediacy spread) add magnitudes and selection caveats. The only formal bargaining theory with82financing is `bian2018bargaining`. No verified paper decomposes the measured gap into a bargaining component (compensation for83delay and failure risk, which depends on seller urgency) and a selection component (mortgage deals require a better match), with84the buyer's outside option differing by financing type. That decomposition is WP19's Proposition 7.8586## 5. Selection, indices and volume–price comovement8788The measurement literature establishes that sold houses are not a random sample of the stock and that the bias co-moves with the89cycle: `jud1994selection`, `gatzlaff1997selection`, `gatzlaff1998selection`, `hwang2004selectivity`, and above all90`goetzmann2006reservation`, where heterogeneous reservation prices make falling volume bias index returns upward and rising volume91bias them downward. In commercial real estate, `fisher2003controlling`, `fisher2007tbi` and `clayton2008liquidity` build92constant-liquidity indices that lead transaction indices and are more volatile; `lin2007illiquidity`, `bond2007marketing`,93`cheng2010illiquidity`, `lin2008real` and `cheng2013liquidityrisk` show that ignoring the uncertain marketing period biases94returns upward and risk downward, and that risk is seller-specific. The classical index tools (`bailey1963repeatsales`,95`case1989efficiency`, `clapp1992assessed`) assume random sampling. Bargaining and information confounders are documented by96`harding2003bargainingpower`, `kurlat2015asymmetries`, `garmaise2004asymmetries`; volume leads prices in `berkovec1996turnover`,97`hort2000turnover`, `clayton2010pricevolume`, `andrew2003transactions`, `leamer2007businesscycle`; `dachis2012transfertax` is the98Canadian transfer-tax experiment.99100The literature recognises the bias but no verified paper derives its *sign* from primitives in a search equilibrium. WP19's101Proposition 6 does so and finds that the sign is not a general law: it depends on whether the elasticity of the sale hazard to102the arrival rate rises or falls with urgency, which is governed by the product of the hazard function and the mean residual103valuation of the match-value distribution. This nuance is new relative to `goetzmann2006reservation` and `fisher2003controlling`.104105## 6. Duration dependence, censoring and withdrawals106107`zuehlke1987duration`, `haurin1988duration`, `jud1996brokerage`, `anglin2003tradeoff`, `knight2002listing`,108`haurin2010listprices`, `huang2001withdrawal`, `benefield2015measurement`, `carrillo2012potatoes` and `anglin2006liquidity`109document the price–time tradeoff, positive duration dependence in parametric hazards without heterogeneity, and the sensitivity110of results to withdrawals and relistings. `taylor1999time` (stigma) and `anenberg2016information` (learning) are competing111explanations of duration effects. WP19's observed hazard is a mixture over urgency types with a competing withdrawal risk112(Lemma 3), which predicts spurious negative duration dependence; the paper must state that positive dependence in the data113requires list-price revisions, learning or stigma, which are outside the model.114115## 7. Foundations and tools116117Diamond–Mortensen–Pissarides architecture (`diamond1982aggregate`, `mortensen1994job`, `pissarides2000equilibrium`,118`mortensen1999new`, `rogerson2005search`, `petrongolo2001looking`, `shimer2005cyclical`); efficiency (`hosios1990efficiency`,119with the two-sided-entry caveat of `gabrovski2025note`); reservation values (`lippman1976economics`); log-concavity120(`bagnoli2005logconcave`); bargaining (`nash1950bargaining`, `rubinstein1982perfect`); directed search (`moen1997competitive`,121`wright2021directed`, `albrecht2016directed`); price dispersion (`burdett1983equilibrium`, `maury2014search`, `sagi2021asset`);122OTC asset-market liquidity premia (`duffie2005over`, `lagos2009liquidity`, `rocheteau2011liquidity`), from which WP19 differs by123the indivisibility and match-specificity of the housing unit; matching evidence (`genesove2012search`: contact-hazard elasticity1240.84 with respect to the buyer–seller ratio under CRS; `carrillo2013heat`, `carrillo2015tightness`, `han2014biddingwars`,125`han2016askingprice`, `merlo2004bargaining`, `merlo2015home`, `horowitz1992role`, `yavas1995strategic`).126127## 8. What is known, what is open, and the model-design implications128129Known: urgency lowers reservation and transaction prices and shortens duration; liquidity is state dependent; credit tightening130lengthens time on market and raises dispersion; cash buyers obtain discounts; transaction indices are selection-biased; tightness131amplifies shocks. Open: (i) a stationary equilibrium with a continuous urgency distribution, free entry and an explicit132frictionless benchmark; (ii) analytical conditions under which a lower arrival or qualification rate lengthens time on market133(monotone hazard) and widens dispersion; (iii) the sign and size of the index selection bias from primitives; (iv) the134decomposition of the cash–mortgage gap into bargaining, outside-option and selection terms; (v) the welfare role of bargaining135power with heterogeneous sellers and one-sided entry.136137Design implications adopted in `model_specification.md`: carrying cost $c$ is a continuous permanent type with a right-skewed138distribution (most sellers patient, a minority urgent, as in the motivation and forced-sale evidence); an exogenous withdrawal139hazard with a positive own-use value $w_0$ (withdrawals and relistings matter empirically); free entry of buyers pins the buyer140value, which both isolates the seller-side comparative statics and generates tightness feedback; the baseline qualification141screen is nested in a financing-contingency extension with a pending state, cash and mortgage buyers, so that $A$ and $\phi$ are142observationally distinct only in the extension; the frictionless benchmark is defined as the Walrasian free-entry price and the143urgency discount relative to the most patient seller is the primary liquidity-discount object, because the forced-sale144literature measures exactly that comparison; match-value distributions include IFR cases (truncated normal, Weibull), the145exponential boundary and a heavy-tailed Pareto case to display the conditions of Propositions 2 and 6; listing inflow is146exogenous (one-sided entry) so that the Hosios benchmark applies in the homogeneous case and heterogeneity is the only wedge.147148## 9. Novelty gate decision149150Propositions 1 and the directional content of 2–4 restate known comparative statics in a new setting and are labelled as151benchmarks in the manuscript, with `albrecht2007opportunistic`, `krainer2001theory`, `lippman1976economics`,152`eerola2018borrowing`, `ngai2014hot` and `novymarx2009hot` credited as antecedents. The contributions that survive the audit are153the joint treatment (continuous urgency × free entry × pending financing), the Pareto counterexample to monotone hazards with154its exact condition, the index-selection proposition with its sign condition, the three-term decomposition of the cash–mortgage155gap, and the numerical welfare analysis with heterogeneous sellers. The gate is passed with these labels; see `novelty_matrix.csv`.156