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Affordable Market Value (AMV)
Affordable market value (AMV) is a mechanism used by the Federal Deposit Insurance Corporation (FDIC) in the United States to make housing more affordable for buyers. It refers to the sale price of a property or housing unit sold through the FDIC’s Affordable Housing Program. The FDIC assigns affordable market value to properties considering the income of a family buying the property and not the appraised value of that property.
A Little More on What is Affordable Market Value (AMV)
FDIC was established to solve the housing needs of communities, especially low-income families. Through the FDIC’s Affordable Housing Program, low-income families and homebuyers are able to access affordable properties. Affordable Market Value (AMV) as used by the FDIC takes into consideration the lower-income purchase requirement to assign a value at which housing units are sold. AMV differs from the appraised value of a property, it is often lower.
Typically, AMV is the value placed on the property by FDIC and not the market value of the property (what buyers are willing to pay). Low-income families who purchase properties through the FDIC’s Affordable Housing Program must agree to rent out units of the multi-faceted apartment to low-Income individuals and households, and at affordable rents.
History of ‘Affordable Market Value (AMV)’
The Affordable Housing Program (AHP) was created as a response to the housing crisis of communities and especially low-income families. The FDIC helps families purchase properties and housing units previously held by failed banks at an affordable rate. The aim of FDIC was to see to the sale of residential properties of failed financial institutions in a prompt manner and at affordable rates to low-income families across the States.
The goal of FDIC’s AHP is similar to the Resolution Trust Corporation (RTC), a corporation that was created to dispose of assets of failed financial institutions. FDIC primarily helped low-income families meet their housing needs by providing affordable housing held by failed banks.
Reference for “Affordable Market Value”
Academics research on “Affordable Market Value”
The Future of the RTC Affordable Housing Disposition Program, Taylor, M. B. (1995). The Future of the RTC Affordable Housing Disposition Program. J. Affordable Hous. & Cmty. Dev. L., 5, 131.
Managing the consequences of financial crisis: A long view of housing disposition, MacDonald, H. (2012). Managing the consequences of financial crisis: A long view of housing disposition. Housing Policy Debate, 22(2), 201-218. Widespread housing foreclosures and the growth of real estate owned inventories impose significant negative externalities on local communities and their residents. An effective “disposition infrastructure” is needed to limit the damage, but historical efforts (in the HOLC and the RTC) and current experience (in the NSP and related programs) suggest this is a challenging task. Central among the challenges faced is that of managing private investor roles in the housing disposition process. Neither regulation nor funding alone is adequate to ensure that foreclosed homes are disposed of in a way that stabilizes rather than undermines neighborhoods. The article concludes by arguing that an effective disposition infrastructure may require a renewed discussion about lender responsibilities to local communities.
Consumption and encounter in (multi) cultural quarters reflecting on London and Rome’s ‘Banglatowns’, Fioretti, C., & Briata, P. (2018). Consumption and encounter in (multi) cultural quarters reflecting on London and Rome’s ‘Banglatowns’. Urban Research & Practice, 1-22. The paper aims at introducing some critical views on ‘multicultural quarters’, reflecting on the cases of Spitalfields in London and Torpignattara in Rome. Urban practices and policies that led these places to be recognised as ‘Banglatowns’ are explored, disentangling two major narratives of multicultural quarters, respectively, commodification of diversity and everyday multiculturalism. Whether literature tends to establish an opposition between these interpretative frameworks, the paper argues that context-based research shows how both categories are interlaced. The coexisting aspects of commodification of ethnicity and encounter are explored, focusing on the human and spatial agents supporting the construction of the image of the (multi)ethnic quarters.
Impact de la responsabilité sociale des entreprises cotées en bourse de Casablanca sur leur performance financière., El Yaagoubi, J. (2019). Impact de la responsabilité sociale des entreprises cotées en bourse de Casablanca sur leur performance financière (Doctoral dissertation, Faculté des Sciences Juridiques, Economiques et Sociales de Fès). Nowadays, we consider social and environmental engagement as a serious strategy between economic actors. In Morocco, this increased interest in CSR is motivated by the label of CSR delivered by the General Confederation of Moroccan Companies. The question is: Is there any impact of this label as a social and environmental engagement on the financial performance of Casablanca Stock Exchange companies? That is the main question of our thesis. We will answer it through a panel data study over the period 2012-2017, using Pooled Ordinary Least Square regression where the explicative variable is a dummy variable (equal 1 if the company is labeled and 0 if not). The variable to explain is financial performance measured by ROA, ROE, ROS and MBV. The control variables are size, industry, age and risk. Keywords: corporate social responsibility, social performance, financial performance, stakeholder theory, CSR label of CGEM.
[PDF] NEURAL NETWORK TO RECOGNIZE THE MARKET VALUE OF THE PRODUCT FOR CUSTOMERS, Bijaksic, S. S., Markić, S. B., & Bevanda, S. A. NEURAL NETWORK TO RECOGNIZE THE MARKET VALUE OF THE PRODUCT FOR CUSTOMERS. This paper analyzes the possibilities of application of artificial neural networks in discovering preferences of consumer into buying products according to their socio-demographic and economic characteristics. The objective of this paper is to improve customer satisfaction on product’s and the revenues of the organizational system. Data set is the result of preprocessing activit y followed by getting the fulfilled surveys from respondents. In the survey are the questions about the socio-demographic characteristics of the customers: gender, age, education and household revenues. The artificial neural networks technique has been applied to classify the products according to the quality, taste, color of the packaging, shape of packaging, price and impression of less chemicals depending on the socio-demographic characteristics of the customers. Artificial neural networks assign the adequate product type to the customers and so help to discover their preferences, satisfaction and recognize the market value of the product for customers. Artificial neural network is built using R language which has shown satisfactory development strength and power.