This paper studies the effects of mortgage subsidies and imperfect competition in the U.S. mortgage market. I exploit discontinuities in interest rates generated by pricing rules and find evidence of advantageous selection. I estimate an industry model that highlights the relationship between mortgage subsidies, intermediary lenders' market power, and borrower's advantageous selection. The model shows that mortgage subsidies enable advantageous selection, creating a deadweight loss of 7.90 billion. Counterfactual analysis reveals that a 50% decrease in lender concentration reduces efficiency by 1.39 billion if mortgages are subsidized, and conversely, increases efficiency by 750.07 million if mortgages are not subsidized.
{"title":"Advantageous Selection with Intermediaries: A Study of GSE-Securitized Mortgage Loans","authors":"Hsin-Tien Tsai","doi":"10.2139/ssrn.3574004","DOIUrl":"https://doi.org/10.2139/ssrn.3574004","url":null,"abstract":"This paper studies the effects of mortgage subsidies and imperfect competition in the U.S. mortgage market. I exploit discontinuities in interest rates generated by pricing rules and find evidence of advantageous selection. I estimate an industry model that highlights the relationship between mortgage subsidies, intermediary lenders' market power, and borrower's advantageous selection. The model shows that mortgage subsidies enable advantageous selection, creating a deadweight loss of 7.90 billion. Counterfactual analysis reveals that a 50% decrease in lender concentration reduces efficiency by 1.39 billion if mortgages are subsidized, and conversely, increases efficiency by 750.07 million if mortgages are not subsidized.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77439578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Traditional data sources for the analysis of housing markets show several limitations, that recently started to be overcome using data coming from housing sales advertisements (ads) websites. In this paper, using a large dataset of ads in Italy, we provide the first comprehensive analysis of the problems and potential of these data. The main problem is that multiple ads ("duplicates") can correspond to the same housing unit. We show that this issue is mainly caused by sellers' attempt to increase visibility of their listings. Duplicates lead to misrepresentation of the volume and composition of housing supply, but this bias can be corrected by identifying duplicates with machine learning tools. We then focus on the potential of these data. We show that the timeliness, granularity, and online nature of these data allow monitoring of housing demand, supply and liquidity, and that the (asking) prices posted on the website can be more informative than transaction prices.
{"title":"What Do Online Listings Tell Us About the Housing Market?","authors":"M. Loberto, Andrea Luciani, Marco Pangallo","doi":"10.2139/ssrn.3176962","DOIUrl":"https://doi.org/10.2139/ssrn.3176962","url":null,"abstract":"Traditional data sources for the analysis of housing markets show several limitations, that recently started to be overcome using data coming from housing sales advertisements (ads) websites. In this paper, using a large dataset of ads in Italy, we provide the first comprehensive analysis of the problems and potential of these data. The main problem is that multiple ads (\"duplicates\") can correspond to the same housing unit. We show that this issue is mainly caused by sellers' attempt to increase visibility of their listings. Duplicates lead to misrepresentation of the volume and composition of housing supply, but this bias can be corrected by identifying duplicates with machine learning tools. We then focus on the potential of these data. We show that the timeliness, granularity, and online nature of these data allow monitoring of housing demand, supply and liquidity, and that the (asking) prices posted on the website can be more informative than transaction prices.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79196323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It has been 70 days since the first case of COVID-19 was detected in the US. Since then it has spread and grown in all but 2 of 376 MSAs and all but 45 of the 636 counties that are contained in these MSA. In this paper we examine the determinants of how rapidly the virus grows once it has been seeded within a MSA or county. We find virus cases can be well predicted by area population, as well as days-since-onset. In the data, virus cases scale almost proportionately with population, and excluding population significantly changes the impact of days-since-onset. Growth is also related to residential density and per capita income, particularly at the county level. There are weaker relationships to MSA average household size, per capita income, and the fraction of the population that is over 65. These results come from parameterizing a simple power function model of cumulative infections since onset. This is shifted proportionately by the various MSA/County covariates. We also experiment with restricting the sample of areas so as to have a minimum number of cases – equal to .01% of the area’s population. This effectively focuses on the more advanced part of the virus growth curve. Here we find a significant further decrease in the coefficient of days-since-onset. This is preliminary evidence that the virus growth is tapering. We intend to repeat our analysis as time progresses.
{"title":"The Geography of COVID-19 Growth in the US: Counties and Metropolitan Areas","authors":"William L. C. Wheaton, Anne Kinsella Thompson","doi":"10.2139/ssrn.3570540","DOIUrl":"https://doi.org/10.2139/ssrn.3570540","url":null,"abstract":"It has been 70 days since the first case of COVID-19 was detected in the US. Since then it has spread and grown in all but 2 of 376 MSAs and all but 45 of the 636 counties that are contained in these MSA. In this paper we examine the determinants of how rapidly the virus grows once it has been seeded within a MSA or county. We find virus cases can be well predicted by area population, as well as days-since-onset. In the data, virus cases scale almost proportionately with population, and excluding population significantly changes the impact of days-since-onset. Growth is also related to residential density and per capita income, particularly at the county level. There are weaker relationships to MSA average household size, per capita income, and the fraction of the population that is over 65. These results come from parameterizing a simple power function model of cumulative infections since onset. This is shifted proportionately by the various MSA/County covariates. We also experiment with restricting the sample of areas so as to have a minimum number of cases – equal to .01% of the area’s population. This effectively focuses on the more advanced part of the virus growth curve. Here we find a significant further decrease in the coefficient of days-since-onset. This is preliminary evidence that the virus growth is tapering. We intend to repeat our analysis as time progresses.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80747088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract We study the spillover of government interventions in the real estate market to the stock market. We find that the more active mutual funds decreased ownership in equities with no short-term reversal. Furthermore, they increased ownership in the finance sector stocks without significant changes to their real estate equity holdings. The interventions affecting the riskiness of the finance sector stocks triggered a larger trading response than the ones focused on the real estate sector stocks’ cash flows. Overall, the spillover of the housing market shocks to the stock market seems to be materialized mostly through the discount rate channel.
{"title":"Government Real Estate Interventions and the Stock Market","authors":"A. Akbari, Karolina Krystyniak","doi":"10.2139/ssrn.3568770","DOIUrl":"https://doi.org/10.2139/ssrn.3568770","url":null,"abstract":"Abstract We study the spillover of government interventions in the real estate market to the stock market. We find that the more active mutual funds decreased ownership in equities with no short-term reversal. Furthermore, they increased ownership in the finance sector stocks without significant changes to their real estate equity holdings. The interventions affecting the riskiness of the finance sector stocks triggered a larger trading response than the ones focused on the real estate sector stocks’ cash flows. Overall, the spillover of the housing market shocks to the stock market seems to be materialized mostly through the discount rate channel.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74869154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. J. Reinders, D. Schoenmaker, Mathijs A. Van Dijk
There is increasing interest in assessing the impact of climate policies on the value of financial sector assets, and consequently on financial stability. Prior studies either take a "black box" macro-modelling approach to climate stress testing or focus solely on equity instruments - though banks' exposures predominantly consist of debt. We take a more tractable finance (valuation) approach at the industry-level and use a Merton contingent claims model to assess the impact of a carbon tax shock on the market value of corporate debt and residential mortgages. We calibrate the model using detailed, proprietary exposure data for the Dutch banking sector. For a €100 to €200 per tonne carbon tax we find a substantial decline in the market value of banks' assets equivalent to 4-63% of core capital, depending on policy choices.
{"title":"A Finance Approach to Climate Stress Testing","authors":"H. J. Reinders, D. Schoenmaker, Mathijs A. Van Dijk","doi":"10.2139/ssrn.3573107","DOIUrl":"https://doi.org/10.2139/ssrn.3573107","url":null,"abstract":"There is increasing interest in assessing the impact of climate policies on the value of financial sector assets, and consequently on financial stability. Prior studies either take a \"black box\" macro-modelling approach to climate stress testing or focus solely on equity instruments - though banks' exposures predominantly consist of debt. We take a more tractable finance (valuation) approach at the industry-level and use a Merton contingent claims model to assess the impact of a carbon tax shock on the market value of corporate debt and residential mortgages. We calibrate the model using detailed, proprietary exposure data for the Dutch banking sector. For a €100 to €200 per tonne carbon tax we find a substantial decline in the market value of banks' assets equivalent to 4-63% of core capital, depending on policy choices.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79245184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Greg Buchak, Gregor Matvos, T. Piskorski, Amit Seru
We study the frictions in dealer-intermediation in residential real estate through the lens of “iBuyers,” technology entrants, who purchase and sell residential real estate through online platforms. iBuyers supply liquidity to households by allowing them to avoid a lengthy sale process. They sell houses quickly and earn a 5% spread. Their prices are well explained by a simple hedonic model, consistent with their use of algorithmic pricing. iBuyers choose to intermediate in markets that are liquid and in which automated valuation models have low pricing error. These facts suggest that iBuyers’ speedy offers come at the cost of information loss concerning house attributes that are difficult to capture in an algorithm, resulting in adverse selection. We calibrate a dynamic structural search model with adverse selection to understand the economic forces underlying the tradeoffs of dealer intermediation in this market. The model reveals the central tradeoff to intermediating in residential real estate. To provide valuable liquidity service, transactions must be closed quickly. Yet, the intermediary must also be able to price houses precisely to avoid adverse selection, which is difficult to accomplish quickly. Low underlying liquidity exacerbates adverse selection. Our analysis suggests that iBuyers’ technology provides a middle ground: they can transact quickly limiting information loss. Even with this technology, intermediation is only profitable in the most liquid and easy to value houses. Therefore, iBuyers’ technology allows them to supply liquidity, but only in pockets where it is least valuable. We also find limited scope for dealer intermediation even with improved pricing technology, suggesting that underlying liquidity will be an impediment for intermediation in the future.
{"title":"Why is Intermediating Houses so Difficult? Evidence from iBuyers","authors":"Greg Buchak, Gregor Matvos, T. Piskorski, Amit Seru","doi":"10.2139/ssrn.3616555","DOIUrl":"https://doi.org/10.2139/ssrn.3616555","url":null,"abstract":"We study the frictions in dealer-intermediation in residential real estate through the lens of “iBuyers,” technology entrants, who purchase and sell residential real estate through online platforms. iBuyers supply liquidity to households by allowing them to avoid a lengthy sale process. They sell houses quickly and earn a 5% spread. Their prices are well explained by a simple hedonic model, consistent with their use of algorithmic pricing. iBuyers choose to intermediate in markets that are liquid and in which automated valuation models have low pricing error. These facts suggest that iBuyers’ speedy offers come at the cost of information loss concerning house attributes that are difficult to capture in an algorithm, resulting in adverse selection. We calibrate a dynamic structural search model with adverse selection to understand the economic forces underlying the tradeoffs of dealer intermediation in this market. The model reveals the central tradeoff to intermediating in residential real estate. To provide valuable liquidity service, transactions must be closed quickly. Yet, the intermediary must also be able to price houses precisely to avoid adverse selection, which is difficult to accomplish quickly. Low underlying liquidity exacerbates adverse selection. Our analysis suggests that iBuyers’ technology provides a middle ground: they can transact quickly limiting information loss. Even with this technology, intermediation is only profitable in the most liquid and easy to value houses. Therefore, iBuyers’ technology allows them to supply liquidity, but only in pockets where it is least valuable. We also find limited scope for dealer intermediation even with improved pricing technology, suggesting that underlying liquidity will be an impediment for intermediation in the future.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80055831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The economic dislocations caused by the coronavirus pandemic will almost certainly result in economic contraction. There have been calls for bold actions to attenuate the effects of this contraction and also to provide cash for struggling Americans whose incomes may be temporarily disrupted. But these actions, including direct cash payments to American households, will add to the federal deficit, and recent large fiscal deficits have tempered enthusiasm for immediate large federal outlays.
The pandemic will lead to a deep contraction that hopefully will be short. The more that national consumption can be smoothed during this time, especially for households that experience the largest temporary hits to their incomes, the quicker the recovery will be. There may be a way to allow American households to help themselves through this period and to lessen the need for more federal spending—by easing some of the restrictions on mortgage lending, to allow Americans to access their single largest asset: their homes. The nation is in the midst of an emergency, desperate for liquidity and for current sources of secured spending. It is worth considering the value of tapping this source of liquidity.
{"title":"Get Cash to More Families that Need It Now: Give Banks More Discretion to Make Home Equity Loans and Refinance Mortgages","authors":"Kevin Erdmann","doi":"10.2139/ssrn.3564490","DOIUrl":"https://doi.org/10.2139/ssrn.3564490","url":null,"abstract":"The economic dislocations caused by the coronavirus pandemic will almost certainly result in economic contraction. There have been calls for bold actions to attenuate the effects of this contraction and also to provide cash for struggling Americans whose incomes may be temporarily disrupted. But these actions, including direct cash payments to American households, will add to the federal deficit, and recent large fiscal deficits have tempered enthusiasm for immediate large federal outlays.<br><br>The pandemic will lead to a deep contraction that hopefully will be short. The more that national consumption can be smoothed during this time, especially for households that experience the largest temporary hits to their incomes, the quicker the recovery will be. There may be a way to allow American households to help themselves through this period and to lessen the need for more federal spending—by easing some of the restrictions on mortgage lending, to allow Americans to access their single largest asset: their homes. The nation is in the midst of an emergency, desperate for liquidity and for current sources of secured spending. It is worth considering the value of tapping this source of liquidity.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77700275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We show empirically that the use of unsecured debt, whose standard covenants commit management to the preservation of debt capacity, leads to lower and more stable leverage. We then show that firm value is sensitive to leverage levels and leverage stability, decreasing in the former and increasing in the latter. Our results support a liquidity-centric version of Jensen's (1986) free cash flow argument. In this version, self-serving managerial tendencies are reigned in without raising leverage indiscriminately, so that financial flexibility is preserved.
{"title":"Financial Flexibility and Manager–Shareholder Conflict: Evidence from Reits","authors":"Timothy J. Riddiough, Eva Steiner","doi":"10.1111/1540-6229.12226","DOIUrl":"https://doi.org/10.1111/1540-6229.12226","url":null,"abstract":"We show empirically that the use of unsecured debt, whose standard covenants commit management to the preservation of debt capacity, leads to lower and more stable leverage. We then show that firm value is sensitive to leverage levels and leverage stability, decreasing in the former and increasing in the latter. Our results support a liquidity-centric version of Jensen's (1986) free cash flow argument. In this version, self-serving managerial tendencies are reigned in without raising leverage indiscriminately, so that financial flexibility is preserved.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73887406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Financial losses due to low demand for parking spaces in garages at residential estates is a key motivation for this research. The purpose of this paper in particular is to statistically explore the relationship between parking occupancy rates and various factors on transport supply, characteristics of location and the building. The occupancy rate of parking was measured as the ratio of actual number of cars to total number of parking spaces. The fieldwork on counting occupied parking spaces was conducted 2 times per day during a week on a sample of 13 locations in different areas of a 1.4-million Yekaterinburg city in Russia. 4700 observed parking spaces give sample size of 173 records. Statistical analysis shows that the crow-fly distance to the city center as well as the number of public transport stops are strongly associated with occupancy rate for parking. Also, occupancy rate is much more affected by the type of parking ownership. Private owning means purchase of a parking space or renting it while public ownership suggests free access. So private parking means a 45% decline in occupancy compared to the public parking regime. Research provides empirical results and some theoretical underpinnings are also highlighted.
{"title":"Exploring Associations between Parking Occupancy Rate at Residential Estates and Spatial Characteristics. The Case of Ekaterinburg","authors":"Yegor Muleev","doi":"10.2139/ssrn.3545934","DOIUrl":"https://doi.org/10.2139/ssrn.3545934","url":null,"abstract":"Financial losses due to low demand for parking spaces in garages at residential estates is a key motivation for this research. The purpose of this paper in particular is to statistically explore the relationship between parking occupancy rates and various factors on transport supply, characteristics of location and the building. The occupancy rate of parking was measured as the ratio of actual number of cars to total number of parking spaces. The fieldwork on counting occupied parking spaces was conducted 2 times per day during a week on a sample of 13 locations in different areas of a 1.4-million Yekaterinburg city in Russia. 4700 observed parking spaces give sample size of 173 records. Statistical analysis shows that the crow-fly distance to the city center as well as the number of public transport stops are strongly associated with occupancy rate for parking. Also, occupancy rate is much more affected by the type of parking ownership. Private owning means purchase of a parking space or renting it while public ownership suggests free access. So private parking means a 45% decline in occupancy compared to the public parking regime. Research provides empirical results and some theoretical underpinnings are also highlighted.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84004432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We ask whether the correlation between mortgage leverage and default is due to moral hazard (the causal effect of leverage) or adverse selection (ex ante risky borrowers choosing larger loans). We separate these information asymmetries using a natural experiment resulting from the contract structure of option adjustable-rate mortgages and unexpected 2008 divergence of indexes that determine rate adjustments. Our point estimates suggest that moral hazard is responsible for 40$%$ of the correlation in our sample, while adverse selection explains 60$%$. We calibrate a simple model to show that leverage regulation must weigh default prevention against distortions due to adverse selection.
{"title":"Selection, Leverage, and Default in the Mortgage Market","authors":"Arpita Gupta, Christopher Hansman","doi":"10.2139/ssrn.3315896","DOIUrl":"https://doi.org/10.2139/ssrn.3315896","url":null,"abstract":"\u0000 We ask whether the correlation between mortgage leverage and default is due to moral hazard (the causal effect of leverage) or adverse selection (ex ante risky borrowers choosing larger loans). We separate these information asymmetries using a natural experiment resulting from the contract structure of option adjustable-rate mortgages and unexpected 2008 divergence of indexes that determine rate adjustments. Our point estimates suggest that moral hazard is responsible for 40$%$ of the correlation in our sample, while adverse selection explains 60$%$. We calibrate a simple model to show that leverage regulation must weigh default prevention against distortions due to adverse selection.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80288652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：