Notice bibliographique
Résumé
Purpose The purpose of this research study is to determine whether flood-damaged residences located in the USA are remaining unrepaired because of the lack of flood insurance coverage. Unrepaired flooded dwellings are subsequently being foreclosed with mortgage-insurance claims being paid to lenders. This paper aims to examine if weather events that cause flooding impact the losses suffered by mortgage insurers and homeowners. Design/methodology/approach Two fully modified least squares regression models are done using losses experienced by two mortgage insurance companies. The AM Best insurance rating information for a 16-year period or years 2002–2017 is used to study whether the loss ratios experienced by two companies underwriting private mortgage insurance (PMI) are statistically correlated to National Flood Insurance Program (NFIP) claim levels. The assumption is that higher flood insurance claims are a proxy for more severe weather events during a particular year which results in flooding that damage residences. Findings The NFIP claims coefficient is positive and significant for both companies being examined. This indicates that the more serious the flooding event during a specific year, the higher the losses experienced by the private mortgage insurer. The R 2 results for the regression models were 0.673–0.695. The income variable has a negative coefficient which was significant. It indicates that falling income lead to rising mortgage insurer losses. The NFIP variable was significant with a positive coefficient. Research limitations/implications The mortgage insurance industry is dominated by several companies at any point in time. During the 16-year study period, some companies have become insolvent, merged with other companies or recently started underwriting mortgage insurance. One company was diversified writing multiple lines of property insurance. There were only two insurers with complete financial information for the specified study period. Practical implications There are currently five mortgage insurers operating in the USA. A serious flood event could cause the insolvency of some of these companies. This would reduce the competition existing in the default insurance market. The financial markets for real estate loans price mortgages based on the availability and the ability to secure mortgage insurance for high loan-to-value properties. There is federal mortgage insurance available for certain types of residential loans. Social implications There are a limited number of insurers writing flood insurance. These companies can pick or reject dwellings and/or commercial properties to underwrite for insurance. The goal of phasing out insurance through the NFIP may prove impossible to achieve. A flood event without insurance would cause serious financial consequences to property owners, loan delinquencies and could depress the local economy for years. Competition from private mortgage insurers may intensify the adverse selection already being experienced by the NFIP. Private insurers would select the lower risk flood applications leaving the more risky insurance to be covered by the NFIP. Originality/value Prior research focused on financial variables impacting PMI and weather factors affecting flood insurance claims. Financial ratios published in the AM Best rating guide for the USA and Canada were used to examine whether or not PMI losses are indirectly affected by flooding events as measured by NFIP variable. Comparing two separate lines of insurance and their impact on each other has not been studied by prior researchers.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».