1# RECORD 11135c13b52c3fe849cfd9899af8e92968906ec52# TITLE: Assessing the Impact of Wildfires on the Swedish Housing Market: a Case Study of the 2014 Västmanland Wildfire3# YEAR: 2026.0 VENUE: Economics of Disasters and Climate Change DOI: 10.1007/s41885-026-00200-34# AUTHORS: Tommaso Piseddu; David Stenvall5# PAGES: 27 CANDIDATE TABLE PAGES (1-based): [10, 13, 14, 16, 20, 21]6# ABSTRACT: The literature on wildfires and residential property prices is limited and primarily focuses on events in North America. There is a lack of studies examining this relationship in Europe. With the largest forest area in the entire EU, understanding this impact is particularly relevant in a Swedish context. In this paper, we investigate how the largest wildfire in Sweden’s recent history, the 2014 wildfire in Västmanland County, affected nearby housing prices and time-on-market. Using a difference-in-differences method, we find a significant negative effect on housing prices. Our most conservative estimate indicates an approximate 2.7% reduction in final selling prices for post-fire sales located within 20 km of the wildfire area. However, the negative effect is even larger when defining the treated area as a 5 km (-10.1%) or 10 km (-8.9%) distance to the fire, and these results are confirmed by a repeated sales model that includes only single- and multi-family houses. In a heterogeneity assessment with respect to the housing types, we find that our effects are mostly driven by the impact on single-family houses rather than apartments. For the former group, we find large negative and significant effects, while we fail to detect any significant one on apartments prices. The largest impact on prices occurs in the first months after the event and for those properties that are located along the trajectory of the wildfire’s smoke.789===== PAGE 1 (score 6, numeric-paren 0, table-words 0, var-words 6) =====1011RESEARCH12Economics of Disasters and Climate Change (2026) 10:2013https://doi.org/10.1007/s41885-026-00200-314Assessing the Impact of Wildfires on the Swedish Housing 15Market: a Case Study of the 2014 Västmanland Wildfire16Tommaso Piseddu1,2 · David Stenvall317Received: 4 February 2026 / Accepted: 12 May 2026 / Published online: 25 May 202618© The Author(s) 202619Abstract20The literature on wildfires and residential property prices is limited and primarily focuses 21on events in North America. There is a lack of studies examining this relationship in 22Europe. With the largest forest area in the entire EU, understanding this impact is particu23larly relevant in a Swedish context. In this paper, we investigate how the largest wildfire 24in Sweden’s recent history, the 2014 wildfire in Västmanland County, affected nearby 25housing prices and time-on-market. Using a difference-in-differences method, we find a 26significant negative effect on housing prices. Our most conservative estimate indicates 27an approximate 2.7% reduction in final selling prices for post-fire sales located within 2820 km of the wildfire area. However, the negative effect is even larger when defining 29the treated area as a 5 km (-10.1%) or 10 km (-8.9%) distance to the fire, and these re30sults are confirmed by a repeated sales model that includes only single- and multi-family 31houses. In a heterogeneity assessment with respect to the housing types, we find that our 32effects are mostly driven by the impact on single-family houses rather than apartments. 33For the former group, we find large negative and significant effects, while we fail to detect 34any significant one on apartments prices. The largest impact on prices occurs in the first 35months after the event and for those properties that are located along the trajectory of the 36wildfire’s smoke.37Keywords Wildfires · Housing prices · Natural disasters · Repeated sales · Sweden38MSC Codes 62J05 · 62J2039JEL Codes C33 · Q51 · Q54 · R3140 41 Tommaso Piseddu42piseddu@kth.se; tommaso.piseddu@sei.org431 44Department of Real Estate and Construction Management, Division of Real Estate Economics 45and Finance, Royal Institute of Technology (KTH), Stockholm, Sweden462 47Stockholm Environment Institute, Stockholm, Sweden483 49Department of Management and Engineering, Division of Economics, Linköping University, 50Linköping, Sweden515253===== PAGE 10 (score 115, numeric-paren 83, table-words 8, var-words 7) =====54551 35620 57Page 10 of 2758Economics of Disasters and Climate Change (2026) 10:2059In other words, the dataset is composed of single observations representing price dif60ferences of two consecutive transactions for the same asset. The treatment group is in this 61approach composed of consecutive prices differences where the earlier transaction occurred 62before the date of the wildfire and the consecutive one occurred after it. That is, the wildfire 63occurred within the temporal bracket of two consecutive transactions.64A threshold distance is again imposed to identify the assets sufficiently close to the wild65fire to be reasonably expected to have been affected by the event. Given that our variable of 66interest, the difference in consecutive selling prices, has been log-transformed, the depen67dent variable of our repeat-sales approach is an approximation of the percentage variation 68of the final selling prices,69Table 2 Main results70Dependent variable:71Log(selling price)72(1)73(2)74(3)75(4)76(5)77Treatment78−0.585***79−0.648***80−0.028**81−0.049***82−0.069**83(0.019)84(0.016)85(0.011)86(0.011)87(0.032)88Post890.278***900.290***910.284***920.282***930.513***94(0.005)95(0.004)96(0.003)97(0.003)98(0.013)99Living Area (m2)1000.005***1010.005***1020.005***1030.005***104(0.0001)105(0.0001)106(0.0001)107(0.00005)108Age109−0.004***110−0.0003***111−0.0004***112−0.002***113(0.0001)114(0.0001)115(0.0001)116(0.00005)117Age21180.00000***1190.00000***1200.00000***1210.00000***122(0.00000)123(0.00000)124(0.00000)125(0.00000)126Number of rooms1270.076***1280.050***1290.052***1300.060***131(0.002)132(0.002)133(0.002)134(0.001)135W(Selling Price)1360.942***1370.949***1380.271***139(0.003)140(0.003)141(0.010)142Distance fire143−0.00000***144−0.00000145(0.00000)146(0.00000)147Distance forest1480.0001***1490.00002***150(0.00000)151(0.00001)152Distance train153−0.00001***154−0.00001***155(0.00000)156(0.00000)157Distance lake158−0.0001***159−0.0001***160(0.00000)161(0.00000)162Post * Treatment163−0.182***164−0.140***165−0.096***166−0.104***167−0.027**168(0.026)169(0.022)170(0.015)171(0.015)172(0.011)173Month F.E.174N175N176N177N178Y179Postal Code F.E.180N181N182N183N184Y185Treatment:186< 20 km187< 20 km188< 20 km189< 20 km190< 20 km191Observations19298,26919398,26919498,26919598,26919698,269197R21980.0621990.3352000.6822010.6942020.829203Adjusted R22040.0622050.3352060.6822070.6942080.826209Note: *p<0.1,**p<0.05,***p<0.01. The following variables are included as controls but omitted from the 210table: Type of housing211212213===== PAGE 11 (score 13, numeric-paren 0, table-words 5, var-words 3) =====2142151 3216Page 11 of 27 21720218Economics of Disasters and Climate Change (2026) 10:20219With respect to a DiD approach, no assumption on the development of the trends is 220needed. However, large datasets are required, with the specific need to span several years. 221For this reason, we leave our original dataset unfiltered and consider all the transactions that 222occurred in Sweden between 2005 and 2019. Another challenge to the application of this 223method comes from the information available through the dataset as the geographic coor224dinates in our dataset represent the centroid of the building structure. This introduces issues 225with apartments, because we are not able to distinguish between them. The geographical 226coordinates are the same for apartments located in the same building and we are not able to 227distinguish among them using other features such as the floor number and their sizes. We 228therefore proceed by dropping the observations pertaining to apartments when employing the 229repeat-sales approach. This represents only a minor issue, considering that most of the assets 230in the proximity of the wildfire are single- or double-family houses. The use of single- and 231multi-family houses only is, however, in line with the standard approaches adopted by the 232literature (Meldrum 2016; Wolf and Klaiber 2017). The use of the repeat sales method further 233reduces the size of the dataset as the assets that were sold only once within it are dropped.234The repeat sales approach is presented in Eq. (2):235 236display-eq-Eq u 2237238(2)239Our dependent variable is therefore the difference between the log-transformed final selling 240price in period t and in period t-1 for each individual asset i. inline-eq-IEq12 is then a dummy 241variable which equals 1 if the wildfire occurred between these two consecutive transactions 242t and t-1. inline-eq-IEq13 is a dummy variable equaling one for the assets located within 10 243km from the perimeter of the wildfire. The rest of the control variables are the same as the 244ones employed in Eq. (2).245Results246Main Results247Table 2 depicts our main DiD results. As previously mentioned, the post dummy variable 248equals one for sales occurring after the wildfire, while the treatment dummy equals one for 249observations located within 20 km from the wildfire’s perimeter. Their interaction dummy, 250inline-eq- I Eq14 , is therefore considering the transactions that occurred after the event 251and within 20 km from this. In the first two columns we use sparse specifications without 252any spatial controls, only including the variables essential for the difference-in-difference 253analysis in column (1), while also adding some basic property characteristics in column (2). 254From these two columns, we begin by observing that prices in our treatment group differ 255from those in our control group, with the property prices in the former group being signifi256cantly lower than in the latter. This is not surprising given that the wildfire took place in a 257rural area far from larger towns or cities, while the control group includes more observations 258in urban areas. Such evidence does not pose any challenge to the validity of our approach, as 259the DiD method assumes the trends to be parallel, which, again, we show to be reasonable 260261262===== PAGE 13 (score 80, numeric-paren 36, table-words 16, var-words 7) =====2632641 3265Page 13 of 27 26620267Economics of Disasters and Climate Change (2026) 10:20268on average, final selling prices that were 10.1% (8.9%) lower than the control group, com269pared to 2.7% lower prices estimated in Table 2. This is shown, respectively, in Column (2) 270and in Column (4) of Table 3. When expanding the treated distance to 25 km and including 271fixed effects, in column (6), we observe that the estimate is only marginally smaller than the 272− 2.7% reported in Table 2. Just like stated previously, the interpretation of the coefficient of 273interest, the interaction between post and treatment is not linear because the dependent vari274able has been log-transformed. From Table 3, we can thus conclude that the effect on hous275ing prices appears to have been most noticeable for sales that occurred in the proximity of 276the wildfire. Overall, the results suggest that the more distant from the wildfire, the smaller 277the impact on housing prices, corroborating the intuition that the wildfire is the event driv278ing the results we observed. It is indeed reasonable to assume that as the treatment group 279is expanded to include asset more distant to the event, as when increasing the threshold 280distance to 25 km, the average effect in this group is diluted. At 27 km of distance from the 281perimeter of the wildfire the impact becomes statistically insignificant.282Robustness Analysis: Treatment Based on Postal Codes283We address in this section the issue related to the arbitrariness with which the distance 284thresholds were defined. The previous section has clarified that the direction of the results, 285i.e. a drop in selling prices, is not affected by the decision on the distance to use. What 286changes is the estimated effect, as more stringent distances identify narrower treatment 287groups that display, in absolute values, larger results. While informed by the previous lit288erature, we acknowledge that the decision on the distance to use is still arbitrary. In order 289to further show that the results are unaffected by this arbitrariness, we employ postal codes 290as an alternative approach for identifying the treatment and the control group. In Table 4, 291we define the sales within the first ring of postal codes closest to the wildfire as the treated 292observations. The columns in this table are best compared with columns (4) and (5) of Table 293Table 3 Robustness analysis – alternative treatment distance294Dependent variable:295Log(selling price)296(1)297(2)298(3)299(4)300(5)301(6)302Treatment303−0.178***304−0.161***305−0.207***306−0.099**307−0.045***3080.096***309(0.038)310(0.055)311(0.028)312(0.045)313(0.010)314(0.020)315Post3160.279***3170.513***3180.280***3190.513***3200.283***3210.514***322(0.003)323(0.013)324(0.003)325(0.013)326(0.003)327(0.013)328Post * Treatment329−0.199***330−0.107**331−0.159***332−0.093***333−0.092***334−0.024**335(0.057)336(0.043)337(0.037)338(0.029)339(0.013)340(0.010)341Month F.E.342N343Y344N345Y346N347Y348Postal Code F.E.349N350Y351N352Y353N354Y355Treatment:356< 5 km357< 5 km358< 10 km359< 10 km360< 25 km361< 25 km362Observations36398,26936498,26936598,26936698,26936798,26936898,269369R23700.6933710.8293720.6943730.8293740.6943750.829376Adjusted R23770.6933780.8263790.6943800.8263810.6943820.826383Notes: *p < 0.1,**p < 0.05,***p < 0.01. The following variables are included as controls but omitted from the 384table: Type of housing, Living area, Age Age2, Number of rooms, W(Price), Distance to fire, Distance to 385lake, Distance to the train386387388===== PAGE 14 (score 61, numeric-paren 23, table-words 14, var-words 7) =====3893901 339120 392Page 14 of 27393Economics of Disasters and Climate Change (2026) 10:203942 which, except for the treatment variable, have the same model specification. This iden395tification strategy, however, introduced significant limitations on the possibility to employ 396postal codes fixed effect, as they would present perfect multicollinearity with the treatment 397variable. The specification is therefore the same as in Eq. (1) with the exception that we can398not exploit postal codes fixed effect. As we acknowledge the utility of maintaining spatial 399fixed effects in our estimations, we here include municipality fixed effects. Such an inclu400sion spares our model from perfect multicollinearity while still allowing for a valid spatial 401control of non-observable factors. Figure 2 provides a visual rendition of this approach.402We find that the effect of the wildfire on housing prices is larger when we use treatment 403based on postal codes rather than distance. This is shown in Table 4. Without fixed effects 404for municipalities, we find a discount of approximately 15%. With such fixed effects, the 405impact is estimated at a statistically significant − 11.9%. We then use sales in both the first 406and second ring of postal codes as the treated observations in columns (3) and (4) of Table 4074. The effects in this table are somewhat lower than in some of the results from Table 3, but 408still highly significant. The results confirm our previous findings of significant impacts from 409the wildfire on final selling prices.410Robustness Analysis: Placebo Tests411In Table 5, we present the results from two different placebo tests. In columns (1) and (2), 412we shuffle the existing distances in our dataset so that each observation is randomly assigned 413a distance from another observation in the data. In column (3) and (4), we instead assign a 414randomly generated distance (between 0 and 100 km) to each observation drawing from a 415uniform distribution. If the observed effects on property prices in Tables 2, 3 and 4 are really 416driven by the wildfire, we expect our interaction term between the post and treatment vari417able to be insignificant in the placebo tests. As depicted by Table 5, the is confirmed by the 418evidence. In the first placebo test (column 1 and 2), both interaction terms are insignificant. 419Dependent variable:420Log(selling price)421(1)422(2)423(3)424(4)425Treatment426−0.280***427−0.194***428−0.102***429−0.0348***430(0.037)431(0.034)432(0.014)433(0.015)434Post4350.280***4360.483***4370.282***4380.4848***439(0.003)440(0.0157)441(0.003)442(0.015)443Post * Treatment444−0.163***445−0.127***446−0.129***447−0.099***448(0.050)449(0.0.045)450(0.018)451(0.014)452Month F.E.453N454Y455N456Y457Postal Code F.E.458N459N460N461N462Municipality F.E.463N464Y465N466Y467Treatment:4681 st Ring 469Postal 470Codes4711 st Ring 472Postal 473Codes4741 st and 4752nd Ring 476Postal 477Codes4781 st and 4792nd Ring 480Postal 481Codes482Observations48398,26948498,26948598,26948698,269487R24880.6944890.75224900.6944910.7521492Adjusted R24930.6944940.75184950.6944960.7517497Table 4 Robustness analysis – 498alternative treatment using zip 499codes500Notes: 501*p < 0.1,**p < 0.05,***p < 0.01. 502The following variables are 503included as controls but omitted 504from the table: Type of housing, 505Living area, Age Age2, Number 506of rooms, W(Price), Distance to 507fire, Distance to lake, Distance 508to train509 510511512===== PAGE 15 (score 10, numeric-paren 0, table-words 4, var-words 2) =====5135141 3515Page 15 of 27 51620517Economics of Disasters and Climate Change (2026) 10:20518In the second test (columns 3 and 4), the magnitudes of the coefficients for interaction terms 519are weak and have the opposite sign of what we found in our earlier results. The coefficient 520in column (3) is insignificant, but we can observe a weakly significant interaction term in 521column (4). Overall, we argue that these tests provide support for the validity of our previ522ous results that identified the wildfire as the main driver of the estimated effects.523Robustness Analysis: Repeat-Sales524In order to provide more robustness to the results presented in the previous sections, we 525address the same research question as before by employing a different method. The trade-526offs that the use of a repeat-sales approach entails have been discussed before in the Method 527section. We just stress that, while on the one hand this method relaxes the stringent assump528tions that are necessary in order to obtain reliable results from a DiD application, on the 529other hand, the number of observations is significantly reduced and the only type of assets 530we can focus on are single- and multi-family houses. This results in a final dataset for this 531analysis with only 5,865 suitable observations. The treatment group used in Table 6 is made 532of couples of consecutive sales that occurred within 10 km from the limits of the wildfire 533and where the first transaction occurred before the wildfire and the following one after it. In 534order to effectively implement this additional strategy, two more variables are created. The 535former, labelled as treatmentd, is a dummy variable equalling 1 for the assets located within 536Fig. 2 Map of the postal codes around the area affected by the wildfire. The red area represents the burnt 537area. The first ring of postal codes that border this are depicted in yellow; the second ring of postal codes 538is in purple. Every green dot represents a transaction539 540541542===== PAGE 16 (score 67, numeric-paren 28, table-words 12, var-words 10) =====5435441 354520 546Page 16 of 27547Economics of Disasters and Climate Change (2026) 10:2054810 km from the perimeter of the wildfire. The latter, treatmentt, indicates the transactions 549pairs where the first transaction occurred before the wildfire and the second one occurred 550after it. The interaction between these two is the key variable of our strategy, equalling one 551for those transactions pairs that occurred within 10 km of the wildfire and where we com552pare the transaction after the wildfire to the one, for the same asset, that occurred before it. 553In a sense, we are estimating how housing prices evolved, by keeping the assets character554istics fixed, i.e. by comparing it to its previous price, before and after the event. The results 555reveal that, on average, the single-family houses in the treatment group experienced a price 556appreciation that was 11% to 17% lower than that experienced by the assets in the control 557group. This one is formed by single-family houses within 10 and 100 km from the wildfire. 558Dependent variable:559Δ(Selling price)560(1)561(2)562Treatmentd563−0.029564−0.051565(0.038)566(0.039)567Treatmentt5680.207***5690.114***570(0.006)571(0.008)572Treatmentd*Treatmentt573−0.183**574−0.115*575(0.073)576(0.070)577Years controls578N579Y580Postal Code F.E.581N582Y583Observations5845,8655855,865586R25870.1715880.257589Adjusted R25900.1695910.248592Table 6 Robustness test – repeat-593sales approach594Notes: 595*p < 0.1,**p < 0.05,***p < 0.01. 596The following variables are 597included as controls but omitted 598from the table: Living area, 599Number of rooms, Distance 600from fire, Distance from forest, 601Distance from lake, Age Age2, 602W(Price), Plot area, Distance 603from train604 605Dependent variable:606Log(selling price)607(1)608(2)609(3)610(4)611(Rand.) 612Treatment6130.0176140.016*615−0.007616−0.007617(0.011)618(0.008)619(0.006)620(0.004)621Post6220.280***6230.513***6240.277***6250.511***626(0.003)627(0.013)628(0.003)629(0.013)630Post * 631(Rand.) 632Treatment633−0.011634−0.0106350.0106360.008*637(0.015)638(0.011)639(0.006)640(0.005)641Month F.E.642N643Y644N645Y646Postal Code 647F.E.648N649Y650N651Y652Treatment:653Re-shuffle654Re-shuffle655Unif. 656Distribution657Unif. Dis658tribution659Observa660tions66198,26966298,26966398,26966498,269665R26660.6916670.8296680.6916690.829670Adjusted R2 0.6916710.8266720.6916730.826674Table 5 Robustness analy675sis – randomized treatment 676assignment677Notes: 678*p < 0.1,**p < 0.05,***p < 0.01. 679The following variables are 680included as controls but omitted 681from the table: Type of housing, 682Living area, Age Age2, Number 683of rooms, W(Price), Distance to 684fire, Distance to lake, Distance 685to train686 687688689===== PAGE 17 (score 9, numeric-paren 0, table-words 4, var-words 1) =====6906911 3692Page 17 of 27 69320694Economics of Disasters and Climate Change (2026) 10:20695Despite the exclusion of apartments, the reduction in the number of observations and the 696change in the method employed, the results from the repeat-sales approach are comparable 697to what is found in Table 2. The fact that the results in Table 6 are slightly higher, may hint 698to the fact that the overall results in Table 2 may be driven by the impact that the wildfire had 699on single-family houses. We investigate such a possibility more in detail in the next section.700Robustness Analysis: Temporal Trends and Heterogenous Effects701The heterogeneity of the impact on single- and multi-family houses and on apartments is 702addressed in the two graphs of Fig. 3. This same figure also explores the temporal trend of 703the impact. Figure 3a) is produced by estimating Eq. (1) and introducing interaction terms 704between monthly dummy variables and the dummy variable equaling 1 for the assets sold 705within 20 km from the limits of the wildfire, i.e. the treatment variable in Eq. (1). In this 706sense, our approach is similar to the one explored by Zhu et al. (2016). By doing so, we 707are not only simply assessing the temporal trend of the estimated effects, but we are also 708Fig. 3 Temporal trends of the estimated impact with respect to the pre-treatment period. Notes: houses in 709blue, apartments in red710 711712713===== PAGE 20 (score 58, numeric-paren 23, table-words 13, var-words 6) =====7147151 371620 717Page 20 of 27718Economics of Disasters and Climate Change (2026) 10:20719Impact on Time-on-Market (TOM)720The list of variables available in the housing transactions dataset this study builds on allows 721us to investigate whether the event had any significant impact on other key indicators of the 722housing market, such as time-on-market (TOM). For some of the observations in our data723set, we have information about the date of posting of the sale and the date of the sale. We are 724therefore able to compute a simple difference between these two variables and measure the 725time the asset spent unsold on the market. It must be cautioned, however, that the informa726tion on the date of posting is not available for every transaction in the dataset, and that we 727therefore result to working with a dataset that is a subset of the original one. After removing 728observations with missing information, our dataset to investigate the impact of the 2014 729Västmanland wildfire on the assets’ TOM drops to 85,558 transactions, with an average 730TOM of 52 days. The estimation proceeds by following the specification in Eq. (1) by sim731ply replacing the dependent variable with the number of days spent on the market unsold.732Table 8 presents the results from investigating this additional channel through which 733the wildfire may have impacted the local housing market in Sweden. The results are in 734line with both the theoretical predictions of search models and with the empirical evidence 735from the previous literature (Wheaton 1990; Krainer and LeRoy 2002; Hayunga and Pace 7362019), highlighting the positive correlation between housing prices and TOM. The wildfire 737is found to have significantly decreased the TOM of the affected areas, with assets located 738within 20 km from its perimeter experiencing an average drop of TOM everything else con739stant, of 10 days (Column (1) in Table 8) or about 16% (Column (2) in Table 6). The findings 740could also be better understood in the context of the literature investigating the impact of 741exogenous shocks on the housing market in Sweden. Piseddu (2025) investigates the impact 742Dependent variable:743Log(selling price)744(1)745(2)746Treatment747−0.042***748−0.068**749(0.011)750(0.031)751Post7520.282***7530.515***754(0.003)755(0.013)756Smoke757−0.164***7580.041759(0.029)760(0.047)761Post * Treatment762−0.100***763−0.022*764(0.015)765(0.012)766Post * Smoke7670.008768−0.0004769(0.040)770(0.030)771Treatment * Smoke772−0.0537730.427***774(0.056)775(0.123)776Treatment * Post * Smoke777−0.085778−0.113*779(0.077)780(0.060)781Month F.E.782N783Y784Postal Code F.E.785N786Y787Observations78898,26778998,267790R27910.6947920.830793Adjusted R27940.6947950.827796Table 7 Assessing the impact of 797wildfire smoke on housing prices798Notes: 799*p < 0.1,**p < 0.05,***p < 0.01. 800The following variables are 801included as controls but omitted 802from the table: Type of housing, 803Living Area, Age Age2,Number 804of rooms, W(Price), Distance 805from fire, Distance from forest, 806Distance from lake, Distance 807train808 809810811===== PAGE 21 (score 71, numeric-paren 40, table-words 7, var-words 12) =====8128131 3814Page 21 of 27 81520816Economics of Disasters and Climate Change (2026) 10:20817of the news of the Fukushima accident on the housing markets surrounding nuclear facili818ties in Sweden and identifies a negative correlation between housing prices and TOM: while 819a drop in housing prices is observed, TOM is found to increase, two signals of increased 820adversity to risk from homebuyers. The diverging evidence between our findings and those 821in Piseddu (2025) could be reconciled by considering the nature of the events studied. In 822the case of the 2014 Västmanland wildfire, homebuyers could count on the fact that the 823forest will grow again and that the supply of natural amenities will be restored, providing a 824justification for the shortened TOM that is observed. In the case of the presence of nuclear 825facilities, the risk will not be removed, resulting in additional negative impacts that affect 826not only housing prices but also TOM.827Lastly, Figure A2 in the Appendix reveal that the wildfire had no significant impact on 828the number of transactions in the affected areas. This is confirmed both in absolute terms 829and in percentages, with the average quarterly number of transactions displaying its usual 830seasonality and the total number of transactions in Q3 and Q4 of 2014 surpassing those of 831Q3 and Q4 of 2015.832Dependent variable:833TOM (Days)834Log(TOM) (Days)835(1)836(2)837Treatment8386.0088390.161840(12.839)841(0.108)842Post843−18.128***844−0.351***845(5.280)846(0.044)847Living Area (m2)8480.206***8490.002***850(0.019)851(0.0002)852Age853−0.225***854−0.002***855(0.031)856(0.0003)857Age28580.001***8590.00001***860(0.0001)861(0.00000)862Number of rooms8630.3848640.020***865(0.487)866(0.004)867W(Selling Price)8682.9178690.083**870(4.155)871(0.035)872Distance fire873−0.0004874−0.00000875(0.0004)876(0.00000)877Distance forest878−0.002879−0.00003880(0.002)881(0.00002)882Distant train8830.001*8840.00001***885(0.0004)886(0.00000)887Distance lake888−0.008***889−0.0001***890(0.001)891(0.00001)892Post*Treatment893−10.334**894−0.164***895(4.586)896(0.039)897Month F.E.898Y899Y900Postal Code F.E.901Y902Y903Observations90485,55890585,558906R29070.0959080.183909Adjusted R29100.0799110.168912Table 8 Assessing the impact of 913the wildfire on time-on-market 914(TOM)915Notes: 916*p < 0.1,**p < 0.05,***p < 0.01. 917The following variables are 918included as controls but omitted 919from the table: Type of housing920 921922923===== PAGE 22 (score 9, numeric-paren 0, table-words 1, var-words 7) =====9249251 392620 927Page 22 of 27928Economics of Disasters and Climate Change (2026) 10:20929Discussion and Policy Implications930The comparison between our results and those produced in other studies introduces the 931need for a discussion on the potential factors behind these. Looking, for instance, at the 932results produced by McCoy and Walsh (2018), our estimate in Table 3, with a treatment 933group made of assets within 5 km from the perimeters of the wildfire in column (2), 934display results that are similar to the 11% contraction that the authors observed for the 935assets within 2 km from the wildfires in Colorado. It must, however, be noted, that our 936results are mostly driven by the short-term impact that the wildfire had in the few weeks 937immediately after, while for the case of Colorado the impact is found to still be signifi938cant even after several months after the event. It must also be noted that the authors focus 939on several wildfires, occurring in areas close to each other, that have a total affected area 940that is about 10 times the size of the event we studied in Sweden. While the area studied 941by McCoy and Walsh (2018) has been affected by several events, we are not aware of any 942other major wildfire in the years preceding or following our period of reference in our 943study area. One could argue that the longer temporal effects identified in Colorado may 944be driven by constant action of several consecutive wildfires that act as a reminder of the 945vulnerability of the area. On the contrary, the wildfire in Västmanland may be perceived 946as a single, unlikely, event in the area. Our results are also in line with some of the esti947mates provided by Mueller et al. (2009) and this study is, to the best of our knowledge, 948one of the first to estimate the impact of the number of wildfires on housing prices. While 949focusing on a different market, in the US, the results presented by the authors corroborate 950the explanation that we provided here: the results reveal that the number of wildfires is 951a significant factor in explaining the impact of climate change risk, and that, while one 952wildfire has a marginal impact of roughly 10%, two wildfires reduce housing prices, on 953average, by 22%. In that sense, the single event in our study area and timeframe may 954make it easier for the housing market to recover significantly quicker than was previously 955estimated.956Another aspect that may explain the relatively quick recovery of housing prices in the 957aftermath of the wildfire is the pace of vegetation restoration. The direct damages from 958the wildfire can be reasonably expected to have impacted prices on the market, but while 959only few assets were actually damaged or destroyed (MSB, 2015), it can be argued that 960the main driver behind the impact of wildfires on housing values is the loss of values in 961the amenities: access and visibility of forests have been previously identified as substantial 962positive drivers of housing prices (Poudyal et al. 2010; Belcher and Chisholm 2018). The 963post-event analysis of the Västmanland fire proposed by Granath et al. (2021) presents evi964dence of a rapid pace of vegetation regrowth in the area. Such an occurrence may explain 965the short-lived impact of the event studied here. The relatively low number of assets dam966aged or destroyed by the event, 71 (MSB, 2015), may also be a factor that explains the 967short-lived impact, compared to other occurrences of more destructive wildfires (Kiel and 968Matheson 2018; Kim and Yoon 2025). Expected utility theory may offer a further explana969tion of the results observed. Most of the assets sold in the rural portions of our dataset are 970intended to be used as summerhouses. If the utility of the buyer is only affected by the use 971of these assets that can be done of them in summer, an event at the end of the season is not 972expected to significantly affect the market for a long period. If the buyer values the utility 973