# Novelty memo — WP36 "The Hidden Dynamics of Used-Car Markets"

Prepared 2026-11-01 from the verified bibliography in `references.bib` (172 entries) and `evidence_matrix.csv`. Citation keys in brackets refer to that file.

## 1. What exists, bucket by bucket

**A. Hedonic prices and quality adjustment.** The hedonic method was invented on automobiles [court1939hedonic; goodman1998court] and canonised on automobiles [griliches1961hedonic; ohta1976automobile], with Rosen's equilibrium interpretation [rosen1974hedonic] and Lancaster's characteristics demand [lancaster1966newapproach] as theory. The index-number literature settles the practical questions WP36 faces: time-dummy versus imputation indexes [silver2007hedonicimputation; diewert2003hedonic], instability of hedonic coefficients and the case for rolling windows [pakes2003hedonic; erickson2011experimental], official-statistics best practice [triplett2006handbook], and the warning that unobserved quality (here, vehicle condition and dealer reconditioning) can contaminate implicit prices [bajari2005hedonic; ekeland2004hedonic]. Vehicle-specific hedonic work prices safety and fuel economy [dreyfus1995timepreference] and establishes that mileage, not age, is the primary depreciation driver [engers2009annualmiles]. BLP [berry1995automobile] remains the structural benchmark for auto demand but is not the natural tool for a listing panel without quantities.

**B. Durable goods and used-car markets.** Since Akerlof [akerlof1970lemons] the economics of used cars has been dominated by adverse selection: theory [kim1985lemons; hendel1999adverse; hendel2002leasing; biglaiser1993middlemen], tests on repairs and trade patterns [bond1982lemons; emons2009usedcars; peterson2014adverse; peterson2017beautiful], the wholesale auction side [genesove1993adverse; larsen2021bargaining], online markets [lewis2011asymmetric; adams2011vettes], and the role of dealers as quality intermediaries [biglaiser2020intermediaries]. A second strand models turnover and prices across vintages in stationary equilibrium with transaction costs [rust1985durable; stolyarov2002turnover; porter1999usedtrade; gavazza2014usedcar]. A third documents how shocks capitalise into used-car prices: gasoline prices [busse2013gasoline; sallee2016fueleconomy], scrappage policy [jacobsen2015scrappage], weather [busse2015weather], and behavioural mileage heuristics [lacetera2012heuristic]. Listing-price data from classifieds appear in European work [kooreman2006anomalies; haan2010internetregional], the latter being the closest precedent for regional segmentation of used-car asking prices (Dutch regions before and after the internet). Canadian evidence is thin: a Transport Canada conference paper finds Quebec vehicles depreciate slightly more slowly than elsewhere [yanes2010depreciation]; the rest is industry reporting [cbb2026depreciation].

**C. Dealer inventory, dynamic pricing and clearance.** Lazear's clearance-sale model [lazear1986clearance] predicts exactly the object WP36 estimates: an initial price that falls with time on the shelf at a rate governed by market thinness. Operations research formalises optimal markdowns for finite inventory with a horizon [gallego1994dynamicpricing; elmaghraby2003dynamicpricing]; empirical analogues exist for supermarkets [pesendorfer2002retailsales; aguirregabiria1999markups], apparel [pashigian1991sale], tickets [sweeting2012perishable], rentals [cho2010flatrental] and house listings [merlo2004bargaining; merlo2015homeselling]. For car dealers, the empirical literature is almost entirely about *new* cars and transaction prices: internet referral and information effects [scottmorton2001internet; zettelmeyer2006internet], promotion pass-through [busse2006cashback], and — most relevant — Israeli, Scott Morton, Silva-Risso and Zettelmeyer's finding that dealers with market power price more aggressively on inventory fluctuations [israeli2022marketpower]. Murry and Zhou [murry2020colocation] show dealer colocation lowers search costs. We found no peer-reviewed study estimating markdown hazards on used-car listings.

**D. Search, dispersion and liquidity.** Dispersion theory [stigler1961information; diamond1971paradox; varian1980sales; burdett1983equilibrium] and measurement [sorensen2000dispersion; lach2002dispersion; baye2006dispersion; kaplan2015dispersion] give WP36 its dispersion metrics. Liquidity is formalised in finance as a search friction [duffie2005otc; lagos2009liquidity] or measured by price impact and spreads [amihud2002illiquidity; amihud1986bidask], and in housing by time-to-sell and matching [wheaton1990vacancy; krainer2001housingliquidity; carrillo2012search; head2014searchliquidity; ngai2014hotcold; piazzesi2020segmented]. Gavazza's aircraft papers [gavazza2011frictions; gavazza2016decentralized] are the closest conceptual template: a durable asset traded in a decentralised, dealer-intermediated market where thinness raises frictions and lowers prices. The housing microstructure literature [han2015microstructure; genesove1997equity; genesove2001lossaversion; guren2018momentum; anenberg2016frictions] provides both the asking-price-as-signal theory and evidence of strategic complementarity in list prices.

**E. Survival analysis.** Standard: Cox and extensions [cox1972regression; andersen1982cox; lin1989robust; grambsch1994proportional; royston2002flexible; therneau2000modeling; kalbfleisch2002statistical], competing risks [fine1999proportional; putter2007tutorial], recurrent events [prentice1981regression], discrete-time hazards for daily panels [allison1982discrete; singer1993discrete], interval censoring for first/last-seen data [turnbull1976empirical], unobserved heterogeneity [heckman1984method], economic duration surveys [kiefer1988duration; lancaster1990econometric].

**F. Spatial competition and regional integration.** [hotelling1929stability; salop1979monopolistic; bresnahan1991entry] (which includes auto dealers in isolated US towns), semiparametric spatial price competition [pinkse2002spatial], Quebec-City gasoline [houde2012spatial], fast food [kalnins2003hamburger], Vancouver gasoline price wars [slade1992vancouver], gasoline dispersion [lewis2008dispersion], and Canadian intranational integration [engel1996border; gorodnichenko2009border; ceglowski2003lawofoneprice]; methods [anselin1988spatial; lesage2009introduction; conley1999gmm; elhorst2014spatial].

**G. Identification of strategic interaction; event studies.** The reflection problem [manski1993reflection; brock2001discrete; bramoulle2009peer; blume2011identification; angrist2014perils; bajari2010static]; gasoline-market evidence on competitor pricing responses and algorithmic repricing [chandra2011gasoline; byrne2019coordinate; assad2024algorithmic]; modern staggered DiD/event-study toolkits [goodmanbacon2021difference; dechaisemartin2020twoway; callaway2021difference; sun2021estimating; borusyak2024revisiting; roth2022pretest; rambachan2023credible; roth2023trending]; clustering [bertrand2004howmuch; cameron2008bootstrap; cameron2011multiway; abadie2023clustering].

**H. Indices and measurement.** Repeat-sales [bailey1963repeatsales; caseshiller1989efficiency; gatzlaff1997selection], hedonic index taxonomy [hill2013hedonic; bils2009quality], listing-price indexes and the list–transaction gap [anenberg2017timely; lyons2019listprices; han2016askingprice; knight2002listing; horowitz1992listprice; haurin2010listprices; anglin2003tradeoff; kurlat2015asymmetries], official used-vehicle CPI components [bls2026usedcars; statcan2022usedvehicles] and industry indices (section 2).

**I. Multiple testing and model evaluation.** [benjamini1995fdr; romano2005stepwise; diebold1995comparing; giacomini2006tests; brier1950verification; graf1999assessment; uno2011cstatistics; harrell2015regression; friedman2001greedy; ke2017lightgbm; lundberg2017shap; athey2019machine; mullainathan2017machine].

## 2. What is known about used-car asking-price dynamics, markdowns and exit

Surprisingly little, academically. The economics literature on used cars overwhelmingly uses *transaction* data (wholesale auctions, state title records, CES purchases, eBay sales). The only large-scale statistics on listing markdowns we could verify are industry: iSeeCars' 2013 analysis of 30 million listings reports that roughly one in five cars is marked down before sale, that dealers cut 1–6 times per listing with a first cut near 5% around day 15, and that listing duration proxies time on market [iseecars2013pricedrops]; AutoTrader.ca reports that 31% of Canadian listings carried a price drop in June 2024 [autotrader2024priceindex]. Biglaiser, Li, Murry and Zhou [biglaiser2020intermediaries] come closest on the academic side, predicting and documenting that the dealer price premium is positively correlated with time on the market, but they work with transaction records, not daily listing paths. Israeli et al. [israeli2022marketpower] show inventory-sensitive dealer pricing for *new* cars. The markdown-hazard framing therefore exists in theory [lazear1986clearance; gallego1994dynamicpricing] and in housing [knight2002listing; merlo2015homeselling; anenberg2016frictions] but has not been estimated on used-vehicle listings in a published paper that we could find. Exit (sold versus withdrawn/relisted) as a competing-risks problem is likewise untreated for vehicles.

## 3. Existing used-vehicle indices and "liquidity" measures

- **Manheim Used Vehicle Value Index** (Cox Automotive): all Manheim US auction sales in 20 market classes; outlier trimming; mileage adjusted by monthly linear regression; mix weights from a 24-month rolling average; Census X seasonal adjustment; rebased January 1997 = 100 in 2023 [manheim2023methodology]. Wholesale, transaction-based, US only.
- **Cox Automotive used-vehicle days' supply**: unsold vAuto listing inventory divided by the trailing 30-day daily sales rate [cox2022dayssupply]. This is a liquidity-type measure (inventory/flow), published monthly for the US; it is a national aggregate with no listing-level hazard, no quality adjustment and no regional detail in the public series.
- **Canadian Black Book Used Vehicle Retention Index**: wholesale average values of 2–6-year-old vehicles as a share of original MSRP, weighted by registrations, adjusted for age, mileage, condition and MSRP inflation [cbb2026index]; companion annual depreciation report with Fitch [cbb2026depreciation]. Wholesale, Canada-wide; no time-on-market dimension.
- **J.D. Power** supplies the dealer-transaction data that both the BLS used-cars-and-trucks CPI (sample of 2–7-year-old vehicles, depreciation-adjusted, mileage adjusted by age since 2024) [bls2026usedcars] and the Statistics Canada used-vehicle CPI component (hedonic time-dummy on log price with log odometer, log age, model fixed effects, by geography and class, introduced with the May 2022 reference month) [statcan2022usedvehicles] use. Both are transaction-based and have no inventory or duration component.
- **CarGurus Instant Market Value** is a listing-comparables valuation, not an index of market liquidity [cargurus2026imv]; **AutoTrader.ca Price Index** is an average listing price adjusted for make and age, with an ancillary share-of-listings-with-price-drops statistic [autotrader2024priceindex]; **iSeeCars** publishes episodic days-to-sell and price-drop studies [iseecars2013pricedrops]. (Black Book US retention indices mirror CBB; the Manheim Market Report is a vehicle-level wholesale valuation, not an index.)

**Does a used-car "liquidity index" already exist?** Not under that name in the academic literature (query 9 in `search_protocol.md` returned only trade guides). But days' supply, days-to-turn and the NADA 30-day inventory target *are* liquidity measures, as are iSeeCars' days-to-sell and AutoTrader's share of price drops. WP36 must acknowledge that an AMLI is a formalisation and extension of these practitioner quantities, not the first attempt to summarise market liquidity for used vehicles. The novelty lies in (i) combining duration, markdown and dispersion dimensions, (ii) estimating them at listing level with hazards rather than ratios, (iii) quality adjustment, (iv) regional/segment disaggregation, and (v) full transparency of construction, none of which the industry series offer.

## 4. What WP36 adds

1. **A listing-level daily panel scraped from dealer websites and portals** (~273k Quebec listings, 138 dealers, 12 Aug–1 Nov 2026) with a complete price-change log and first/last-seen dates. No published economics paper uses dealer-website asking-price paths for used cars; European precedents [kooreman2006anomalies; haan2010internetregional] use static classifieds, housing papers use MLS logs.
2. **Markdown hazards and recurrent-event models for price cuts** (Andersen–Gill / Prentice–Williams–Peterson) and **competing-risks exit** (sold-type removal vs withdrawal/relisting), with interval censoring. This implements Lazear/Gallego–van Ryzin markdown theory on vehicles for the first time we can document.
3. **Competitor response**: event-study estimates of how a dealer's price cut propagates to comparable listings at nearby dealers, with the reflection-problem and staggered-treatment toolkits of bucket G. Guren's strategic complementarity in list prices [guren2018momentum] and gasoline-market reaction studies are the precedents; nothing exists for used-car listings.
4. **Quebec regional segmentation** of dispersion, liquidity and markdown behaviour (Montréal, Québec, Outaouais, regions), connecting to Canadian intranational integration work [engel1996border; gorodnichenko2009border; ceglowski2003lawofoneprice] and the Dutch regional used-car result [haan2010internetregional]; the only prior Quebec-specific vehicle evidence is [yanes2010depreciation].
5. **A transparent, reproducible AMLI**: quality-adjusted (hedonic, rolling window, à la Statistics Canada) listing-price component plus duration, markdown-frequency and dispersion components, published with code, by region and segment — in contrast to proprietary indices whose weights and adjustments are only summarily disclosed.

## 5. What WP36 cannot add (honest limits)

- **No transaction prices.** Asking prices are commitments/signals [han2016askingprice; horowitz1992listprice], and the list–transaction gap varies with market conditions [lyons2019listprices; knight2002listing]. WP36 cannot measure realised prices, negotiated discounts, trade-in credits or financing terms, and cannot identify adverse selection in the Akerlof sense.
- **Exit is not sale.** A listing disappearing may mean a sale, a wholesale disposal, a transfer between dealer sites, or de-listing; exit hazards are hazards of removal, and any "sold" label is inferred. Competing-risks results must be presented as such.
- **Short window (≈12 weeks) and one season.** Seasonality [ngai2014hotcold; busse2015weather] and macro shocks [busse2013gasoline] cannot be separately identified; the AMLI is a cross-sectional/short-panel object, not yet a time series comparable to Manheim or CBB.
- **Dealer and portal coverage** is a sample of Quebec dealers' websites and aggregators, not the universe; private-party sales and wholesale channels are absent; selection into listing on scraped sites may bias dispersion.
- **Condition and reconditioning are unobserved** [bajari2005hedonic], so hedonic residual dispersion mixes true price dispersion with quality dispersion.
- **Competitor-response identification** rests on timing and spatial proximity; without exogenous shocks to a single dealer's costs, estimates are of conditional co-movement, with the usual reflection-problem caveats [manski1993reflection; angrist2014perils].
- **No welfare or structural counterfactuals** of the Gavazza–Lizzeri–Roketskiy type; WP36 is descriptive/reduced-form.

## 6. Closest prior works (ranked)

1. Biglaiser, Li, Murry & Zhou (2020, RAND) — dealers as intermediaries; dealer premium vs time on market [biglaiser2020intermediaries].
2. Israeli, Scott Morton, Silva-Risso & Zettelmeyer (2022, Management Science) — dealer dynamic pricing in response to inventory [israeli2022marketpower].
3. Haan & de Boer (2010, De Economist) — regional price differences in used-car listings before/after the internet [haan2010internetregional].
4. Gavazza, Lizzeri & Roketskiy (2014, AER) and Gavazza (2011, AER) — transaction costs/frictions and liquidity in used-durable markets [gavazza2014usedcar; gavazza2011frictions].
5. Knight (2002, Real Estate Economics) and Anenberg & Laufer (2017, REStat) — listing-price changes, time on market and listing-based indexes in housing, the methodological template WP36 transposes to vehicles [knight2002listing; anenberg2017timely].