When Does Tourism Raise Land Prices? Threshold Effects, Superstar Cities, and Policy Lessons from Japan

Daisuke Murakami; Mingzhi Xiao; Takara Sakai; Yuki Takayama

REVIEW 5 major objections 7 minor 4 references

When Does Tourism Raise Land Prices? Threshold Effects, Superstar Cities, and Policy Lessons from Japan

T0 review · 5 major / 7 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read Tourism raises land prices only in the top 5.9% of Japanese destinations

desk verdict A solid, mostly believable national-scale threshold result in Japanese land markets, but the domestic-only tourism measure and the in-sample threshold search mean the specific 4.45M/5.9% headline is not yet secure. read the letter →

arxiv 2509.04307 v1 pith:R5R3Q4RW submitted 2025-09-04 econ.GN q-fin.EC

Mingzhi Xiao , Takara Sakai , Daisuke Murakami , Yuki Takayama This is my paper · ORCID

classification econ.GNq-fin.EC

keywords tourismlandpricesthresholdeffectssuperstarcitiesJapanpanelregressionmediationanalysisspatialheterogeneity

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish when, and for whom, tourism actually pushes up land prices in Japan. Analyzing all 1,724 municipalities from 2021 to 2024, it claims that the link is not uniform: below roughly 4.45 million annual tourist arrivals, tourism has no measurable effect on municipal land prices, while above that threshold the effect turns positive and significant. The gains are concentrated in about 5.9 percent of municipalities—the 'superstar' destinations—where the estimated elasticity reaches about 0.33. The paper also proposes that the expansion of accommodation and food-service businesses is the main channel through which tourism raises land prices. A sympathetic reader would care because the claim, if right, reframes tourism policy: most places need not fear tourism-driven housing costs, while a small set of cities needs targeted intervention.

What carries the argument

The central object is the tourism-arrivals threshold estimated by panel threshold regression, a method that endogenously finds a tipping point in the regressor. The paper identifies a threshold near 4,450,000 annual tourist arrivals per municipality, dividing municipalities into low-tourism and high-tourism regimes with different land-price elasticities. The complementary stepwise grouping analysis locates the same regime shift by estimating separate coefficients across quantile cutoffs, and the mediation analysis uses accommodation and food-service sector size as the intervening channel.

What would settle it

Re-estimate the same threshold regression on a 2015–2019 pre-COVID panel or with municipality-level international arrivals added: if the below-threshold tourism coefficient becomes positive and significant, or the estimated threshold moves far outside the reported 4.29–4.62 million confidence interval, the concentration claim would be falsified.

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Extended reading notes

Core claim

The paper's central claim is a threshold: using Hansen panel threshold regression on a 2021–2024 municipal panel, tourist arrivals affect land prices only after annual arrivals exceed about 4,450,000. Below that cutoff the tourism coefficient is 0.011 and statistically insignificant; above it the coefficient is 0.064, and among large high-tourism municipalities it reaches about 0.33. Stepwise grouping regressions confirm that significance appears only in the uppermost quantiles of tourist arrivals, with high-group coefficients of 0.146–0.359. Mediation analysis through accommodation and food-service establishments and employment is presented as the primary transmission mechanism: including t

Load-bearing premise

The load-bearing premise is that annual domestic tourist arrivals, measured as residents traveling at least 20 kilometers from home via smartphone location data, adequately captures total tourism pressure on municipal land markets—a measure that excludes international visitors and short-distance trips and overlaps the post-COVID recovery window.

Editorial extensions

If this is right

For the roughly 94% of Japanese municipalities below the threshold, tourism growth is not a source of land-price pressure, so affordability concerns and anti-tourism housing policies would be largely misplaced there.
For superstar destinations above the threshold, tourism-driven land-price appreciation is substantial, making targeted policies—progressive property taxation, short-term rental regulation, and affordable housing expansion—the relevant response.
The mediation result implies that the land-price effect operates through the expansion of accommodation and food-service capacity, so policies that shape that sector are the main lever for influencing tourism's housing-market impact.
Local tax revenue rises about 0.07% for every 1% increase in tourist arrivals, but the paper finds the distribution of those gains unknowable with its aggregate data, so fiscal benefits alone do not show broad-based resident welfare gains.
Non-residential land prices respond less strongly than overall land prices, suggesting tourism demand mainly pressures the housing side of local land markets.
The heterogeneity results caution against uniform national tourism-led development strategies, which would either neglect superstar-city pressures or over-restrict most municipalities.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Because the paper's tourism proxy counts only domestic residents traveling at least 20 km, including international visitors and day-trippers could lower the threshold and enlarge the superstar set, especially for cities like Kyoto and Osaka where inbound tourism is a major demand component.
The negative coefficients found in small, tourism-intensive municipalities suggest displacement or capacity constraints; transaction-level or rental microdata could test whether incumbent residents are being priced out in those places.
The 2021–2024 window overlaps the post-COVID reopening, so the estimated threshold may partly reflect recovery dynamics; extending the panel backward to pre-2020 would reveal whether the 5.9% cutoff is structurally stable or a rebound artifact.
The near-complete mediation by accommodation and food-service capacity suggests a natural stronger test: instrumenting service-sector expansion with historical tourism assets or natural amenities to sharpen the causal claim.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Worth knowing: this is the first all-municipality Japanese panel (1,724 municipalities, 2021-2024) linking tourist arrivals to land prices, and it finds a clean threshold: below roughly 4.45M annual arrivals the tourism coefficient is near zero and insignificant; above it, 0.064, and in the top group the elasticity reaches 0.33. The baseline FE elasticity (0.039-0.048) is stable across controls, lags, and placebo permutations. That is real and useful. The mediation story - accommodation/food-service expansion as the channel - is plausible and the first-stage is strong.

But the central threshold claim has a measurement problem that the paper acknowledges only vaguely. The tourism variable counts domestic residents traveling at least 20 km, so it excludes inbound visitors and short trips. In Kyoto, Osaka, and Nara, inbound tourism is a major demand component; omitting it compresses the measured intensity exactly in the cities that should define the high regime. The threshold value and the top-5.9% classification are therefore not invariant to the measure. The 2021-2024 window also spans the post-COVID reopening, so the threshold may partly capture recovery-driven domestic trips rather than a stable structural break.

The stepwise grouping results are less clean than the narrative suggests. At the 95th and 97th percentile cutoffs, the low group's tourism coefficient is positive and significant (0.014 and 0.019), which cuts against the claim that effects are confined to the top 5.9%. The Hansen threshold is estimated in-sample with several quantile cutoffs searched; the confidence interval is tight but the specification search is real.

The mediation section overreaches. Once the AFS mediator is included, the tourism coefficient turns negative; the paper reads this as full mediation, but the mediator is likely a bad control or collider, and the design does not support a causal mediation claim. The tax-revenue exercise is a nice addition, but distributional conclusions are rightly hedged.

Who is this for? Applied urban/tourism economists and Japanese policymakers. The paper deserves a serious referee - it assembles a valuable dataset and asks the right question - but the revision needs a sensitivity analysis using a tourism measure that includes international arrivals (or at least a bound on the bias), a less definitive claim about the exact threshold, and a more cautious mediation interpretation. I would not cite the specific threshold numbers as they stand.

Referee Report

5 major / 7 minor

Summary. The paper uses a 2021–2024 panel of 1,724 Japanese municipalities to estimate the elasticity of municipal land prices with respect to annual tourist arrivals. Baseline fixed-effects regressions yield a small positive elasticity (0.039–0.048). A Hansen panel threshold regression is reported to find a cutoff near 4,450,000 arrivals: below it the coefficient is 0.011 (statistically insignificant), above it 0.064. Stepwise quantile groupings and heterogeneity by city size are used to argue that significant effects are concentrated in the top 5.9% of 'superstar' destinations, with the elasticity reaching 0.33 in large high-tourism cities. Mediation analysis points to accommodation and food services as the main channel, and robustness checks (lags, permutation placebo) are presented.

Significance. If the threshold result were robust, the paper would provide rare nationwide evidence for pronounced nonlinearity in the tourism–land price relationship and would speak directly to place-based policy debates in tourism-dependent economies. The paper’s assembly of a comprehensive municipality-level panel, its use of fixed effects, and its transparent handling of missing data are clear strengths. However, the headline threshold and the 5.9% classification rest on a domestic-only tourism measure and on threshold/search specifications whose reported evidence is partly contradictory. These issues must be resolved before the central claim is secure.

major comments (5)

[3.2, 5.3] The tourism regressor counts only domestic residents traveling at least 20 km from home; international visitors are excluded. In superstar destinations such as Kyoto, Osaka, and Nara, inbound tourism is a major demand component. Because the threshold θ* and the 5.9% classification are estimated from this mismeasured regressor, both are potentially non-invariant to the inclusion of inbound arrivals. Section 6.2 acknowledges the absence of expenditure data but does not quantify the bias from omitting inbound tourism. Please provide a sensitivity analysis using a measure that includes international visitors (e.g., prefecture-level inbound statistics merged with municipal shares, or a domestic+inbound proxy) or, failing that, a formal bound on how large the omitted inbound component would have to be to move the threshold materially.
[5.3, Table 6] Table 6 shows that at the 95th and 97th percentile cutoffs, the low-tourism group coefficient is positive and statistically significant (0.014, p=0.027; 0.019, p=0.002). This directly contradicts the text’s statement that 'significant positive impacts only emerge in municipalities with tourism levels above the identified cutoff.' The high-group coefficients in those rows are also imprecisely estimated. Please reconcile these numbers or soften the 'concentrated exclusively' conclusion. In addition, the 'top 5.9%' figure is not tied to the reported quantiles: the 95th percentile gives a 5% high group, the 97th gives 3%, and the empirical percentile of the Hansen threshold is not reported. Please report the percentile of θ* and the number of cross-threshold observations.
[5.3, Table 7] The Hansen threshold regression as reported omits essential inference details: the likelihood-ratio statistic for the threshold effect, its bootstrap p-value, and the construction of the reported 95% confidence interval for the threshold value. Without these, the reader cannot distinguish a statistically significant threshold from a search over many candidate cutoffs. Additionally, Hansen (1999) assumes a non-dynamic panel and an exogenous threshold variable; the paper should state whether those conditions hold and discuss the implications of the short, post-COVID 2021–2024 window, which the limitations section notes.
[5.2, Table 4] When the AFS mediators are added, the tourism coefficient becomes negative and statistically significant (-0.015), not merely attenuated toward zero. This sign reversal is not consistent with the text’s claim that the service sector 'almost fully accounts' for the land-price effect; it suggests that the mediators may be bad controls (outcomes of tourism that also determine land prices) or that the mediation model is misspecified. Please discuss this reversal explicitly, state the sequential ignorability assumption, and consider sensitivity analyses that treat AFS as a mediator rather than a control in a structural sense.
[4.5, 6.2, A.1] The paper claims that lagged specifications and permutation placebo tests 'support a causal interpretation' and 'unlikely to be driven by spurious correlation.' These exercises cannot rule out time-varying confounders or reverse causality, as Section 6.2 appropriately acknowledges when discussing endogeneity. Please temper the causal language in Section 4.5 and Appendix A.1 so that the conclusions match the limitations stated in the paper itself.

minor comments (7)

[Table 2] VIFs are reported for the full model only. If VIFs are intended to show multicollinearity, report them for all specifications or note that they are similar.
[Table 1, 4.1] Land price N=6,080 but the baseline sample is N=4,750. Listwise deletion is mentioned, but a brief flow diagram or a column showing the sample for each regression would clarify the drop.
[Figures 2, 3] Figures 2 and 3 are referenced in the text but do not appear in the manuscript. Please ensure the figures are included and properly labeled.
[Equation in 4.3(a)] The second line of the threshold regression equation appears typeset incorrectly: it is missing the coefficient on log(Tourism) and the control vector notation is incomplete. Check the equation.
[Section 4.4] The notation 'QβM/0)11,βM2)3*-R' appears corrupted; it should read as a vector of group-specific coefficients. Please fix the formatting.
[References] Yoshida and Kato (2024) is cited in the text, but the reference list gives 2023. Please harmonize the year and the volume/issue details.
[A.2] The sdmTMB interpolation is used only for presentation. Please state explicitly that no spatial spillovers or spatial econometric estimates are being made, so readers do not misinterpret Figure A2 as evidence of spatial spillovers.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the tourism–land price threshold is an estimated empirical parameter, not an input-derived prediction, and no load-bearing self-citation chain is present.

full rationale

The paper is an empirical panel study, not a derivation from first principles. The central threshold (≈4,450,000 annual tourist arrivals) is estimated endogenously from the data using Hansen (1999) panel threshold regression, and the 'top 5.9%' summary is a descriptive restatement of where that estimated threshold falls in the sample distribution of tourist arrivals. This is in-sample estimation, not a fitted parameter renamed as a prediction, and the paper does not claim out-of-sample forecast power for the threshold. The baseline elasticities (0.039–0.048), threshold-regime coefficients (0.011 below, 0.064 above), and heterogeneity results are all estimated coefficients reported with standard errors and fixed effects; none is defined into existence by a variable definition. The mediation analysis uses standard product-of-coefficients logic and explicitly acknowledges that the AFS channel is a scale-type proxy, not an exhaustive mechanism. The paper's limitations section candidly notes the short 2021–2024 window, reliance on arrivals rather than expenditure, endogeneity concerns, and lack of micro-data; these are measurement and identification concerns, not circularity. There are no meaningful self-citations: the authors do not cite their own prior work for any load-bearing premise, uniqueness theorem, or ansatz. The literature review invokes prior threshold and 'superstar destination' results, but the paper's own empirical estimates are independent of those citations. Nothing in the derivation chain reduces to its own inputs by construction.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central estimates rest on standard econometric identifying assumptions plus a specific measurement premise about domestic tourist arrivals. The threshold and subgroup cutoffs are fitted in-sample, and the mediation analysis requires a strong exogeneity assumption that is not defended. There are no newly invented theoretical entities.

free parameters (3)

Tourism threshold theta* = about 4,450,000 annual arrivals (log threshold estimated by Hansen 1999)
Estimated from the same panel and used to define the superstar regime; central to the headline claim.
Stepwise quantile cutoffs = 80th, 90th, 95th, 97th percentiles (plus median/mean/tertiles)
Chosen by authors to search for a significant split; no multiple-testing correction, and low-group effects become significant at the 95th and 97th cutoffs.
High-tourism subgroup in heterogeneity analysis = top 5.9 percent (n=163 large; n=32 small)
The split that produces the 0.331 large-city elasticity and the -0.598 small-city elasticity; the small-cell estimates are fragile.

assumptions (5)

domain assumption Panel fixed-effects identification: conditional on municipality and year effects and controls, log tourist arrivals is mean-independent of the error term.
Invoked in the baseline model in Section 4.1; the paper itself acknowledges possible reverse causality and omitted time-varying confounders in Section 6.2.
domain assumption No time-varying confounders correlated with both tourism recovery and land prices during 2021-2024.
The threshold and elasticity estimates rely on the post-COVID panel being a stable environment; the paper does not instrument for tourism.
domain assumption Mediation identification: the accommodation/food-service mediator is exogenous conditional on controls, so its coefficient can be interpreted as a causal mediation channel.
Used in Section 4.2; the mediator is likely endogenous to local demand, and the paper labels the evidence as consistent rather than causal in Section 5.2.
domain assumption Hansen threshold model regularity: threshold variable exogenous and errors satisfy conditions for bootstrap LR inference.
Section 4.3(a); standard assumptions from Hansen 1999, not tested here.
domain assumption Tourist arrival measure captures relevant tourism activity.
Section 3.2 defines domestic 20+ km trips only; excludes international and short-distance visitors, which matters for superstar destinations.

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Cite this review

Pith. "Pith review of When Does Tourism Raise Land Prices? Threshold Effects, Superstar Cities, and Policy Lessons from Japan." pith.science (2026). https://pith.science/paper/R5R3Q4RW

@misc{pith2026250904307,
  author       = {Pith},
  title        = {Pith review of: When Does Tourism Raise Land Prices? Threshold Effects, Superstar Cities, and Policy Lessons from Japan},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/R5R3Q4RW}},
  note         = {Machine review of arXiv:2509.04307}
}

read the original abstract

While tourism is widely regarded as a catalyst for economic and urban transformation, its effects on land prices remain contested. This study examines tourism and land prices using a panel of 1,724 Japanese municipalities from 2021 to 2024, with annual tourist arrivals as a proxy for tourism activity. Using mediation analysis and panel threshold regression, we show that sizable land price increases are concentrated in a small group of "superstar" cities, specifically those in the top 5.9 percent for tourist arrivals, while most municipalities experience little or no effect. The results highlight pronounced nonlinearities and spatial heterogeneity in tourism's economic impact across Japan. The potential mechanisms linking tourism to land price growth are mixed, with possible benefits for local residents as well as risks of increased burdens. These findings underscore the need for policies that promote inclusive growth and an equitable distribution of tourism-related gains.

Figures

Figures reproduced from arXiv: 2509.04307 by the authors.

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Works this paper leans on

4 extracted references · 4 canonical work pages

[2]

negative externalities

Literature Review The impact of tourism on land prices has become a central focus of international research in urban studies, regional science, and applied economics. Classic theoretical land use frameworks (Alonso, 1964; Fujita, 1989; Duranton & Puga, 2015) posit that increased demand for proximity to amenities, including tourist attractions, raises the ...

work page 1964
[3]

Theoretical Framework and Data 3.1 Theoretical Framework and Hypotheses Based on the literature reviewed above, the conceptual framework guiding this study (Figure 1) integrates classic theories from urban economics with recent empirical evidence on the impact of tourism to land markets in Japan. As a theoretical foundation, the framework draws on the bid...

work page 1964
[4]

)=𝛼#+𝛽#𝐿𝑜𝑔(𝑇𝑜𝑢𝑟𝑖𝑠𝑚!

Methodology This section outlines the empirical strategies used to investigate the impact of tourism development on land prices in Japanese municipalities. We describe the baseline regression approach, mediation analysis, threshold effect identification, heterogeneity analysis, and robustness checks. 4.1 Baseline Panel Fixed Effects Regression To estimate...

work page 1999
[6]

superstar

Discussion and Limitations 6.1 Discussion This study provides clear nationwide evidence that the relationship between tourism and municipal land prices in Japan is characterized by marked nonlinearity and strong spatial variation. Our empirical results, visually summarized in Figure 4, demonstrate that significant land price appreciation is highly concent...

work page 2017

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [2]

negative externalities

Literature Review The impact of tourism on land prices has become a central focus of international research in urban studies, regional science, and applied economics. Classic theoretical land use frameworks (Alonso, 1964; Fujita, 1989; Duranton & Puga, 2015) posit that increased demand for proximity to amenities, including tourist attractions, raises the ...

work page 1964

[2] [3]

Theoretical Framework and Data 3.1 Theoretical Framework and Hypotheses Based on the literature reviewed above, the conceptual framework guiding this study (Figure 1) integrates classic theories from urban economics with recent empirical evidence on the impact of tourism to land markets in Japan. As a theoretical foundation, the framework draws on the bid...

work page 1964

[3] [4]

)=𝛼#+𝛽#𝐿𝑜𝑔(𝑇𝑜𝑢𝑟𝑖𝑠𝑚!

Methodology This section outlines the empirical strategies used to investigate the impact of tourism development on land prices in Japanese municipalities. We describe the baseline regression approach, mediation analysis, threshold effect identification, heterogeneity analysis, and robustness checks. 4.1 Baseline Panel Fixed Effects Regression To estimate...

work page 1999

[4] [6]

superstar

Discussion and Limitations 6.1 Discussion This study provides clear nationwide evidence that the relationship between tourism and municipal land prices in Japan is characterized by marked nonlinearity and strong spatial variation. Our empirical results, visually summarized in Figure 4, demonstrate that significant land price appreciation is highly concent...

work page 2017