Proposes 'STR housing assetization' as a better concept than gentrification or touristification for STR-driven urban change, and illustrates it with the 2019 Valencia InsideAirbnb data (rent gaps, professional hosts, high guest densities, displacement estimates).
Thesis (C1) STR-driven urban change is best conceptualised as 'STR housing assetization' — a distinct urban accumulation-by-dispossession strategy — rather than as gentrification, touristification or tourism gentrification; this term has greater explanatory power. partial
A thoughtful conceptual contribution whose empirical half re-uses the 2019 Valencia dataset. The descriptive evidence on rent gaps, professional concentration and spatial clustering is consistent with the concept, but the features that would distinguish 'assetization' from touristification are not measured, and the most striking numbers (9,047 displaced, ≥27.89% supply reduction) are assumption-driven upper-bound scenarios presented as minimums.
Claims (13)
193
Issues (9)
324
Numeric checks (10)
5311
Summary
The paper argues that STR-led urban change is best understood not as gentrification (no upper-class replacement) nor touristification (not the traditional tourism industry) but as a specific form of housing assetization: STRs raise exchange value, yield and liquidity of housing (escaping tenancy law), attracting capital. Three elements define the strategy: STR-induced rent gaps, emergence of professional intermediaries, and profit-maximising practices (tourist areas, small flats, high guest density). Displacement is decomposed into direct, exclusionary and pressure forms, with STR displacement said to be primarily financial and to target tenants, implying class antagonism. Empirically it re-uses the 31 July 2019 Valencia InsideAirbnb scrape (7,203 listings) with AirDNA occupancy, Idealista rents and INE stock: rent gap €497-958/month on average; top-gap districts €10.8-11.5k/year; tourist districts generate 51.8% of STR revenue; multi-listing hosts 58.54% of listings; guests per bedroom higher for multi-hosts and small flats; top-10% earners get 52.79% of revenue. It estimates 3,663 homes extracted and ~9,047 people directly displaced, rental supply in the two tourist districts reduced by ≥27.89% and ≥14.09%, and argues that STR harms should be addressed by housing rather than tourism policy.
Research questions
Is 'STR housing assetization' a more suitable concept than gentrification/touristification for STR-driven change?
Can the accumulation strategy be observed empirically in the STR market of a city (Valencia)?
Stated contribution
A new concept (STR housing assetization) with a three-element mechanism and an STR-specific typology of displacement, plus an empirical illustration.
How the argument is built
Literature: financialization/assetization → STRs raise rent and liquidity → professionalisation → touristification → displacement typology and class analysis. Methods: Valencia 2019 data. Analysis: rent gap; professional hosts; densities; revenue concentration; displacement estimates. Discussion: concept, class antagonism, housing-policy implications.
Argument map
Claims (boxes) coloured by how well the paper supports them; concepts as ellipses. Arrows point from premise to what it supports.
Thick black border = thesis; blue border = conclusion/recommendation. Click a node to jump to it. Layout engine: graphviz.
STR-driven urban change is best conceptualised as 'STR housing assetization' — a distinct urban accumulation-by-dispossession strategy — rather than as gentrification, touristification or tourism gentrification; this term has greater explanatory power.
Assessment: The conceptual argument is coherent and well grounded in the financialization literature, and the distinctions drawn (who is displaced; role of tenancy law) are genuinely useful. But 'greater explanatory power' is never tested: the Valencia evidence (rent gaps, professionalisation, concentration) would be equally consistent with the rival framings, and the asset-specific features (liquidity, capital flows into housing, investor ownership) are not measured.
STRs increase housing's rent yield (rent gap) and its flexibility/liquidity as an asset because they escape tenancy law, making STRs a mechanism of housing financialization.
Assessment: The yield side is supported by the rent-gap estimate; the liquidity/flexibility side is argued from literature (Kemp; Dagkouli-Kyriakoglou et al.) with no Valencia evidence (e.g. sales, holding periods, investor purchases).
Converting a dwelling to STR in Valencia yields on average €497-958/month more than residential letting (over €10.7k/year in the top four districts); the rent gap remains high in tourist districts despite high supply, because tourist demand is strong.
Assessment: A positive gap is plausible. Magnitude depends on un-tested assumptions (70 m2, asking rents, list prices, uniform district occupancy). The method differs from Gil (2021) on the identical dataset (which reported €500-1,200) without explanation. The demand-based explanation for persistent gaps is conjecture from a single cross-section.
STR markets are controlled by a small number of professional actors: multi-listing hosts are 28.34% of hosts but hold 58.54% of listings; 15 hosts hold 660 listings.
Assessment: Counts reconcile (see Gil 2021 audit); the 71.91% single-host share is a carried-over error (should be 71.66%).
Professional hosts raise yields through higher guest densities: more guests per bedroom among multi-listing and high-availability hosts and in small flats; small flats are over-represented among multi-hosts; this creates displacement pressure and 'low-cost' tourism.
Assessment: The descriptive differences are reported (2.05 vs 2.36 guests/bedroom), but they measure maximum permitted guests, not actual occupants; differences are modest; the link to displacement pressure is asserted from literature. The paper equates this density with 'occupancy rate', the same term used for booked-night occupancy, creating ambiguity. 'Yield' is not computed per host type.
About 3,663 homes were extracted from the rental market and ~9,047 people directly displaced by STRs in Valencia over the last decade; in the discussion this becomes 'a minimum of 9,047 residents'.
“It was estimated that a minimum of 9,047 residents have suffered this form of displacement in Valencia.”
Assessment: Arithmetic is correct (3,663 x 2.47 = 9,048) but the estimate assumes every entire home listed >60 days previously housed a tenant household of average size that was displaced. Former owner-occupied, second, vacant, newly built or purpose-built tourist units would all inflate the count, while the snapshot cannot date conversions to 'the last decade'. The figure is an upper-bound scenario, yet the discussion calls it a minimum.
In the two tourist districts STRs equal 39.6% and 27.46% of rental stock and reduced residential rental supply by at least 27.89% and 14.09%; together with 54-58% rent rises (2013-19) this shows STRs reduce supply and increase rents, causing exclusionary displacement.
Assessment: Same stock-ratio logic as Gil (2021) plus an assumption that multi-host listings were 'highly probably' withdrawn from residential rental — presented as a lower bound. Rent rises are co-temporal but not attributed: Ciutat Vella's increase was only the fourth highest, so districts with far fewer STRs rose faster, which weakens the causal reading the paper then states ('As a result, STRs both reduce ... and increase ...').
STRs generate displacement pressure only where traditional tourism already does (Ciutat Vella: capacity 22.77% of residents); elsewhere they do not create coexistence problems or deteriorate livability.
Assessment: Inferred from capacity ratios alone; no data on noise, complaints, retail change or resident perceptions. The negative claim ('do not create coexistence problems') is as unsupported as a positive one would be, and sits in tension with the paper's own earlier statement that STRs deteriorate community life within buildings (a building-level effect that does not require area-level concentration).
STR displacement is primarily financial and affects tenants (homeowners may leave with a capital gain), so STR conflicts express class antagonism between asset holders and tenants rather than residents vs tourists.
Assessment: Logically coherent distinction and a useful contribution; empirically untested here (no tenure data on displaced persons).
Most entire homes are likely extracted from the residential market because 74.75% are rented >60 days a year.
Assessment: 'Rented' here actually refers to calendar availability; a 60-day threshold is lower than the 90- or 182-day thresholds used in earlier papers on the same data; 'extracted from the residential market' is assumed from literature.
The more listings a host has, the more they concentrate in tourist areas (r falls from 0.98 to 0.74).
Assessment: Direction plausible, but correlating each band's distribution with the overall distribution that contains that band is a part-whole correlation (inflating r for large bands); the relation is not monotonic (single hosts r = 0.916 < 2-5 hosts r = 0.980); 'significant' is claimed without test or n (≈19 districts).
Revenue is concentrated: the top 10% of hosts earn 52.79% and operate disproportionately in Ciutat Vella and small dwellings; these actors have the most negative impact on the housing market.
Assessment: Concentration figures are consistent (and close to Gil 2021's 52.81%). The 'most negative impact' inference is not measured. Over-representation units are unclear (percentage points vs relative %).
STR harms should be addressed through housing policy (protecting residential use) rather than tourism/zoning policy; under strict housing regulation STR rent gaps 'cannot emerge'. Evidence: during the pandemic STRs did not return to the residential market.
Assessment: Follows from the framework. The 'impossible' claim is overstated. The pandemic evidence is in tension with other work in the corpus (Gil 2021 and Sequera et al. 2022 report some owners moving STRs to residential or mid-term rental).
Mixed. Conceptual argument followed by a single-city, single-snapshot descriptive quantitative illustration (same dataset as Gil 2021, Cuadernos Geográficos).
Rent-gap estimate (price x district occupancy − 15% commission − 20% management vs 70 m2 x rent/m2)
Pearson correlations of district distributions by host band
Back-of-envelope displacement estimate (listings >60 days x household size)
Transparency notes
Assumptions stated; rent-gap method differs from Gil (2021) on the same data without comment; correlation inputs (district vectors) not reported; no significance tests shown though 'significant' is claimed.
Limitations acknowledged by the authors
'It is impossible to determine the number of direct displacements' — the figure is an estimate.
Rent increases 'cannot be attributed exclusively to STRs'.
Pre-pandemic scrape chosen deliberately.
Data sources
ID
Source
Type
Description
Coverage
Loc.
D1
InsideAirbnb Valencia, 31 July 2019
scraped platform data
7,654 listings; 451 inactive and 46 long-stay removed; 7,203 analysed.
Valencia city, one date (last pre-pandemic scrape).
p. 9
D2
AirDNA district occupancy
scraped platform data
District occupancy rates applied to all listings in a district.
p. 9
D3
Idealista rents
other
District asking rent per m2 (2019 without Nov-Dec; three districts missing); 2013-2019 rent changes.
pp. 9, 14
D4
INE housing stock / population; GVA household size
official statistics
Rental stock and population by district; average household size 2.47.
pp. 9, 13
D5
Literature on financialization, STRs, touristification and displacement
Table 2: guests, price per night, per guest and per bedroom by bedrooms; guests per bedroom 2.05 (single hosts) vs 2.36 (>20 listings); 2.1 vs 2.23-2.34 by availability; multi-hosts over-represented in studios (+21.66%) and 1-bed (+5.84%).
Revenue concentration: top 10% 52.79%, top 20% 67.53%; top earners 35.96% in Ciutat Vella (+14 points) and over-specialised in studios (+2.18%) and 1-bed (+13.57%).
STRs = 39.6% and 27.46% of rental stock in Ciutat Vella and Poblats Marítims; estimated supply reduction ≥27.89% and ≥14.09%; rents rose 54.17% and 57.89% in 2013-2019 (4th and 1st highest).
An urban accumulation-by-dispossession strategy in which STRs increase the exchange value, yield, flexibility and liquidity of housing as an asset, attracting capital and professional intermediaries.
coined
Defined mostly by its effects; the 'liquidity/asset-class' component is not operationalised in the empirical section.
K2
STR-induced rent gap
pp. 3-4, 9
Difference between STR income and residential rent for the same dwelling.
adapted
Smith (1996); Wachsmuth & Weisler (2018)
K3
Direct / exclusionary displacement / displacement pressure
pp. 6-7
STR adaptation of Marcuse's typology via Cocola-Gant (2016).
adapted
Marcuse (1985); Cocola-Gant (2016)
K4
STR 'occupancy rate' (guests per bedroom)
pp. 11-12
Maximum number of guests allowed relative to bedrooms, compared with average household size.
operational
The same term is used for AirDNA's booked-days occupancy rate; the two meanings are conflated in places. Guest capacity is a maximum, not actual use.
Issues (9)
I1majorconceptualConcept's distinctive features not operationalised
What distinguishes 'assetization' from rent-gap-driven touristification (liquidity, capital switching, investor ownership, asset-class treatment) is not measured: the data are listing-level only, with no ownership, transaction or capital-flow information. Hence the empirical section cannot adjudicate between the proposed concept and rival framings, and 'greater explanatory power' is asserted rather than demonstrated.
Impact on conclusions: The concept remains a plausible theoretical proposal; the Valencia case illustrates but does not test it.
I6majorovergeneralizationDisplacement estimate presented as a minimumpp. 13, 16
The 9,047 figure assumes every high-availability entire home displaced a tenant household; it is explicitly an estimate on p. 13 ('are believed to', 'are thought to') but becomes 'a minimum of 9,047 residents' in the discussion. The same 'at least' framing is applied to the supply-reduction figures, which rest on the assumption that multi-host listings were withdrawn from residential rental.
Impact on conclusions: Headline displacement numbers should be read as upper-bound scenarios.
I8majorcausal inferenceCausal claims about rents and livability from cross-sectional ratiospp. 13-16
Rent increases are co-temporal only (and Ciutat Vella ranked 4th), and livability effects are inferred from capacity ratios; both are then stated as outcomes ('As a result, STRs ... increase residential rental prices'; 'does not deteriorate community livability').
Impact on conclusions: These conclusions are hypotheses consistent with the data.
I2moderatetransparencyRent-gap method changed from earlier paper on identical datap. 9
Gil (2021) used price x available days x occupancy − 20% and found €500-1,200/month; this paper uses price x occupancy − 15% commission − 20% and finds €497-958/month, without noting the change or reconciling results. Key assumptions (70 m2, asking rents, list prices) are unchanged and untested.
Impact on conclusions: Magnitude uncertain; a positive gap likely.
I5moderatemeasurementGuest capacity treated as actual occupancypp. 11-12
'Occupancy rate' is used both for booked-night share (AirDNA) and for maximum guests per bedroom; the latter is compared with average household size (2.47) as if it were actual occupants. Also '40% / 30% / 20%' of listings by guests per bedroom sum to 90%.
Impact on conclusions: Density differences are suggestive only.
I3minorarithmeticCarried-over data errorspp. 9-11, 13
Sample arithmetic (7,654 − 451 − 46 ≠ 7,203) and the 71.91% single-host share (should be 71.66%) are repeated from Gil (2021). Also 3,663 homes vs 74.75% x 4,920 = 3,678.
I7minormeasurementModelled revenue and 'impact' language
Revenue and 'yield' are modelled from district occupancy and list price; impact on the residential market is inferred, not measured. Over-representation units (percent vs points) are ambiguous.
'It is therefore impossible for STR-induced rent gaps to emerge under a strict housing regulation' is an unqualified deduction; the cited pandemic evidence is in tension with other corpus papers that report some STR-to-residential switching.
Impact on conclusions: Recommendation direction reasonable; certainty overstated.
Scope internal = numbers printed in the document; cross paper = other corpus documents; external / replication = outside sources or re-analysis of original data (web mode only).
21.55, 20.70, 17.16, 15.04, 15.49 vs reported 24.82, 23.10, 17.74, 15.36, 15.49. Consistent only if per-guest is a mean of listing-level ratios (legitimate but then inconsistent with the per-bedroom column, which is a ratio of means). Not documented.
Evaluation frame: A conceptual paper with an empirical illustration. The concept should be judged on coherence, distinctiveness and usefulness; the empirical section on whether it actually shows the mechanism specific to the concept, and whether estimates are framed with appropriate uncertainty.
Dimension
Score
Rationale
conceptual clarity
4
Clear three-element mechanism and a useful displacement typology with a class dimension; 'occupancy rate' ambiguity.
logical coherence
3
Argument well structured, but empirical findings do not discriminate between the proposed and rival concepts; some internal tensions (livability).
evidential support
2
Descriptive indicators are solid; displacement, supply-reduction and rent-effect claims rest on strong assumptions.
methodological rigor
2
Single snapshot, part-whole correlations, capacity used as occupancy, back-of-envelope displacement estimate.
transparency
3
Assumptions stated, but method change vs 2021 not disclosed and carried-over errors.
proportionality
2
Estimates turned into 'minimum' counts; 'impossible' policy deduction.
Strengths
Clear theoretical positioning in the financialization literature.
Tenant-vs-homeowner differentiation of displacement is insightful.
Explicit assumptions; honest caveat that displacement cannot be measured directly.
Useful policy reframing (housing vs tourism policy).
Weaknesses
Concept not empirically discriminated from rivals.
Displacement and supply-loss estimates overstated as minimums.
Re-use of 2019 data with undisclosed change in rent-gap method and repeated errors.
Measure asset-related features: ownership type, purchases for STR, holding periods, capital sources.
Present displacement as a range with lower/upper assumptions (tenure before conversion).
Use panel data on rents and STR density with controls.
Reconcile rent-gap method with Gil (2021).
Links to the rest of the corpus
X1contradictionmoderateDid pandemic STRs return to the residential market?
Gil (2023) states that during the pandemic 'STRs did not return to the residential market (Gil et al., 2023)' and uses this to argue for housing-policy (not tourism-policy) responses. Gil, Vidal & Yrigoy (2026) cite the same source for STR units being 'temporarily redirected to residential or mid-term segments'. Sequera et al. (2022) document central Madrid long-term listings rising from 1,300 to 4,600 and back to 1,900, and Gil (2021) reported hosts 'directly ending the business and renting in the residential market'. The evidence supports a temporary switch, largely under seasonal contracts; the 2023 paper's unqualified statement understates it.
Implication: The policy argument in Gil (2023) should be qualified: switching occurred but was temporary and often outside long-term residential contracts.
X4data reusemoderateSame Valencia 2019 scrape, undisclosed change of rent-gap method
Gil (2021) and Gil (2023) analyse the identical 31 July 2019 InsideAirbnb Valencia dataset. Errors propagate (7,654 − 451 − 46 ≠ 7,203; single-host share 71.91% instead of 71.66%). The rent-gap formula changes (availability-based vs occupancy with 15% commission) giving €500-1,200 vs €497-958 per month, without acknowledgement; the 'not habitual residence' threshold shifts from 90/182 days to 60 days.
Implication: Readers cannot tell which estimate is preferred; magnitudes are assumption-driven.
X5method driftmoderateShifting thresholds for 'commercial' / 'non-residential' listings
Availability >182 days defines commercial listings in Gil (2021) and Gil & Sequera (2020); Gil (2021) also uses >90 days to argue non-residence; Gil (2023) uses >60 days to estimate homes 'extracted from the rental market' and displacement.
Implication: Lower thresholds mechanically raise displacement estimates; sensitivity analysis is never reported.
X10citation chainmoderateThe 'policy package' causal claim becomes a premise
Gil & Martínez (2021) attribute the 2014-2020 boom to the 2013 SOCIMI/LAU/privatization package, post hoc. Later papers state as settled that 'the reforms achieved their objective' (Gil & Palomera 2024), that the package 'contributed to generating expectations of profitability' (Vidal et al. 2024), and build periodisations on it (Martínez & Gil 2022; Gil 2023). The 2025 special-issue introduction instead foregrounds QE, low rates and Basel III as drivers of GCL expansion.
Implication: A contested interpretation gains apparent robustness through self-citation; the programme's own later synthesis implicitly weakens it.
Gil (2021): Valencia 2019 supply structure, concentration and rent gap. robust
Later corpus papers that rely on this one
Gil et al. (2026d): Liquidity/flexibility as a motive for landlords to choose less regulated rental forms. fragile
Provenance
Analyst: Claude (Anthropic), LLM-generated audit · 2026-11-08 · access: document only · text: text/gil_2024_assetization_valencia.txt · schema v1.1
No external sources were consulted (offline run). Figures are assumed to be correctly reported; claims[].external, citation checks and external/replication checks were not performed.
PDF page 1 is the publisher cover; article page n = PDF page n+1. Shares the dataset of gil_2021_valencia_airbnb.