The Allocation of Capital Between Residential and Nonresidential Uses: Taxes, Inflation and Capital Market Constraints

We have constructed a simple two-sector model of the demand for housing and corporate capital. An increase in the inflation rate, with and with- out an increase in the risk premium on equities, was then simulated with a number of model variants. The model and simulation experiments illustrate both the tax bias in favor of housing (its initial average real user cost was 3 percentage points less than that for corporate capital) and the manner in which inflation magnifies it (the difference rises to 5 percentage points without an exogenous increase in real house prices and 4 percentage points with an exogenous increase). The existence of a capital-market constraint offsets the increase in the bias against corporate capital, but it introduces a sharp, inefficient reallocation of housing from less wealthy, constrained households to wealthy households who do not have gains on mortgages and are not financially const rained. Widespread usage of innovative housing finance instruments would overcome this reallocation but at the expense of corporate capital. Only a reduction in inflation or in the taxation of income from business capital will solve the problem of inefficient allocation of capital. The simulation results are also able to provide an explanation for the failure of nominal interest rates to rise by a multiple of an increase in the inflation rate in a world with taxes. When the inflation rate alone was increased, the ratio of the increases in the risk-free and inflation rates was 1.32. An increase in the risk premium on equities, in conjunction with the increase in inflation, lowered the simulated ratio to 1.10, introduction of a supply price elasticity of 4 and an exogenous increase in the real house price reduced the ratio to 1.03, and incorporation of the credit-market. constraint reduced the ratio to 0.95.

Inflation and the Benefits from Owner-Occupied Housing

This paper examines the effects of inflation on the allocation of resources between residential and nonresidential uses and the productivity of capital in the U.S. We begin by calculating the realized rates of return on homeowner equity and the contributions of fixed-rate mortgages and differences in relative inflation rates to extraordinary earned real returns. The paper then focuses on the implications of the extraordinary real returns on residential capital for stock prices and on the demand for owner-occupied housing. Proposals for achieving efficient allocation of capital between residential and nonresidential uses are also considered.

The Economics of Mortgage Terminations: Implications for Mortgage Lenders and Mortgage Terms

The paper begins with the development of models explaining the mortgage refinancing and assumption decisions of households Having identified the economic variables influencing these decisions, we then simulate the models for different values to determine under what conditions households will refinance or assume. Finally, we draw some implications of these results for: (1) the impact of a decline in mortgage rates on the asset portfolio yields of mortgage lending institutions and (2) the effect of the observed rise in interest rate volatility, including the optimal terminations response of mortgage borrowers, on the terms of the mortgage contract and the returns to mortgage lenders on recently issued mortgage loans.

Anticipating Daily Intention using On-Wrist Motion Triggered Sensing

Anticipating human intention by observing one's actions has many applications. For instance, picking up a cellphone, then a charger (actions) implies that one wants to charge the cellphone (intention). By anticipating the intention, an intelligent system can guide the user to the closest power outlet. We propose an on-wrist motion triggered sensing system for anticipating daily intentions, where the on-wrist sensors help us to persistently observe one's actions. The core of the system is a novel Recurrent Neural Network (RNN) and Policy Network (PN), where the RNN encodes visual and motion observation to anticipate intention, and the PN parsimoniously triggers the process of visual observation to reduce computation requirement. We jointly trained the whole network using policy gradient and cross-entropy loss. To evaluate, we collect the first daily "intention" dataset consisting of 2379 videos with 34 intentions and 164 unique action sequences. Our method achieves 92.68%, 90.85%, 97.56% accuracy on three users while processing only 29% of the visual observation on average

Diethyl 2,5-diphenyl­furan-3,4-dicarboxyl­ate

In the title compound, C22H20O5, the substituted benzene rings are twisted away from the furan ring, making dihedral angles of 54.91 (14) and 20.96 (15)° with the furan ring. The dihedral angle between the two benzene rings is 46.89 (13)°. One ethyl group of one eth­oxy­carbonyl unit is disordered over two sets of sites with occupancies of 0.56 (12) and 0.44 (12). In the crystal, weak intra­molecular C—H⋯O hydrogen bonds link the mol­ecules into chains along the c axis

Identification of the melting line in the two-dimensional complex plasmas using an unsupervised machine learning method

Machine learning methods have been widely used in the investigations of the complex plasmas. In this paper, we demonstrate that the unsupervised convolutional neural network can be applied to obtain the melting line in the two-dimensional complex plasmas based on the Langevin dynamics simulation results. The training samples do not need to be labeled. The resulting melting line coincides with those obtained by the analysis of hexatic order parameter and supervised machine learning method