Price Growth Research Articles

Monitoring and Regulating Food Prices is the Conceptual Foundation for the Sovereignization of Food Security in Russia: More on the Egg Problem and the Role of the State

This article contains the author’s reflections on the effectiveness and adequacy of the measures of the Russian state in assessing the situation and efficient intervention in regulating prices for the most important food products. The article was inspired by a very heated (and in fairness, unfortunately, unproductive) discussion in December last year of the explosive growth in prices for birds’ eggs and egg production. The article shows (based on the study and analysis of primary data and a number of expert assessments) some possible basic reasons for the growth of consumer prices for a food egg, which, however, can be extrapolated to food issues in general or separately by its types; the conclusions and proposals of the author of a conceptual nature regarding state regulation measures both in relation to the stabilization of the price situation in the Russian food market and the introduction of eff ective mechanisms of state support for commodity producers are given; formation of an integral system of price monitoring of food products is shown; systemic changes and additions in a number of regulatory legal acts and development and program documents of strategic planning are given.

The 3 Decades Global Financial Policy Failure in Housing Market, Runaway Home Prices, Today’s Growing Homeless Crisis: Eddison Walters Real Estate Housing Technology Structural Change Transformational Theory

Failure to address technological-driven structural changes in the real estate market that began three decades ago has caused runaway home prices that developed countries are experiencing today. The real estate crash that preceded the Global Financial Crisis, and the entire Global Financial Crisis resulted from the panic that was created about the rapid increase in home prices that was falsely labeled a real estate bubble by the media which was quickly adapted by policymakers. Policymakers and the media blamed greedy mortgage brokers for the rapid increase in home prices, accusing mortgage brokers of providing mortgages to too many people for homes that the borrowers could not afford. It turns out that the media and policymakers were completely wrong when mortgage brokers were falsely blamed for creating a real estate bubble because a real estate bubble never existed. The true cause of the rapid increase in home prices was the early stages of a real estate boom from technological-driven structural changes in the real estate market. Failure to address structural changes in the real estate housing market which started three decades ago, has resulted in runaway home prices in the United States and developed countries around the world. As a result of the failure widespread economic hardship from homelessness is a significant risk as working families find themselves priced out of the housing market. Three decades of economic policy failures and the mismanagement of the housing sector have resulted in a looming housing crisis that continues to increase as developed countries face runaway home prices. Policymakers throughout developed countries eagerly accepted the media-driven misinformation of a real estate bubble without any evidence presented supporting the narrative. The failure of policymakers to gain an understanding of factors that have been driving the rapid increase in home prices which began three decades ago, resulted in the real estate crash, Global Financial Crisis, and Great Recession. Much of the economic hardship and the looming housing crisis facing working families today could have been avoided. For many working American families, the prospect of home ownership no longer exists due to skyrocketing home prices. The looming homeless crisis facing the developed world today is the direct result of economic policy failures that have ignored the structural changes in the housing market for three decades. The move from print media real estate listings to online real estate listings and the adaptation of online mortgage loan applications along with automated mortgage underwriting between 1995 and 1999, exponentially increased the quantity of inventory needed in the housing market of developed countries. The shift in the demand curve for housing structurally changed the housing market in developed countries resulting in the complete transformation that saw the demand for housing skyrocket by 2000. New homebuyers who previously only had access to local housing markets before the transformation of the housing market began in 1995, had full access to homes listed anywhere online in other real estate markets by the end of 1999. The increased demand caused the rapid growth in home prices. Failure to implement policies to drastically increase home inventory in developed countries to address the structural transformation of the housing market which drastically increased the demand for homes was a significant economic policy failure. Instead of implementing policies to significantly increase home inventory, new construction was idle for over a decade as policymakers adopted the media-driven misinformation of a real estate bubble and supported policies to address the nonexistent real estate bubble.

Drivers of European housing prices in the new millennium: demand, financial, and supply determinants

Many countries in Europe have experienced a steady increase in housing prices over the past decade, which continued even during the recent crisis. We analyze a panel of 15 European countries over the period 2000–2020. We find that demand-side determinants, such as GDP, unemployment, wage and population, strongly influence housing prices. Nevertheless, we suggest that construction costs, access to finance (credit to GDP), and financing costs (long-term interest rate) should be included to avoid biased results. We find that financial development can significantly affect housing prices in the long run. We confirm the robustness of our results by conducting a lag sensitivity analysis of selected determinants. In addition, we find a negative effect of the GFC and a positive effect of the Covid crisis on housing prices. Furthermore, we find that countries with a mild reaction to or a quick recovery from the GFC experienced significantly higher housing price growth.

Characteristics of household energy consumption in the shadow of the Russia-Ukraine war - a case study from Hungary

This study aims to identify the factors affecting household energy consumption by applying spatial econometric models. Our findings suggest that sharp energy price growth is followed by significant reductions in household energy consumption, but it is difficult to distinguish specific consumer groups. Weather conditions are of particular importance (compared to the EU average). The results highlight two things: a) the problem of obsolete housing stock and the need for energy efficiency improvements and b) low energy awareness of households, especially before the energy crisis. Furthermore, hotspots with higher per capita gas consumption are also identified in Hungary. The geographical dimension is crucial, and it is not possible to make general decisions on energy efficiency issues. Effective results can be achieved through spatially concentrated interventions.

Why is the Cobb-Douglas production function so popular?

It is well known that, in continuous time, the Cobb-Douglas function can be derived from the underlying, data governing, accounting identity under some reasonable assumptions (factor shares are constant, and the weighted growth of the labour input price and the capital input price is constant). In this article these results are generalized in three ways: (1) the accounting identity contains a (pure) profit term; (2) continuous time is replaced by discrete time periods; (3) additional assumptions appear to be superfluous. The article also discusses extensions: from two to multiple inputs, from value added to gross output, and from a single production unit to an ensemble of those units.

USE OF MACHINE LEARNING TOOLS AND PYTHON LIBRARIES FOR PREDICTION THROUGH REGRESSIONS

The purpose of this work is forecasting the average check amount depending on the time spent by customers in the retail area and forecasting the average check amount based on seasonal demand characteristics considering the constant annual price growth. Forecast methods were analysed. The use of Python libraries was considered. The code was developed for solving the above-mentioned forecasting tasks through linear, polynomial and random forest regressions, specifically, the search for the dependence of the average check amount on the time spent by customers in the retail area and the dependence of the average check amount on seasonal price fluctuations over several years. A comparison of the results of linear and polynomial regression was made under conditions of smaller or larger volumes of the input data, explanations were provided regarding the appropriateness of choosing one or the other when modelling non-cyclical processes and why the random forest model provides adequate results for cyclical processes. The source code and examples of input data were shared for public access. Scientific novelty lies in the ways of applying regressions for modelling and forecasting the average check amount depending on the time customers spend in the retail area as well as the average check amount depending on seasonal demand characteristics with consideration of constant price growth. The selected examples of cyclical and non-cyclical economic phenomena are the most common ones, and practitioners face similar tasks daily. Therefore, the results of this work provide them with ready-made solutions that require minimal adaptation to practical needs. Additionally, it demonstrates the feasibility of deploying them in the AWS cloud environment and the potential for integration with various data sources and other information systems.

Growth and Instability Analysis of Pepper with Reference to Production and Price

Black pepper, the most traded spice in the world is native to South India. Though Pepper is popularly known as black gold, it does not provide the value of gold to its cultivators because of several persisting problems. Market imperfections, illegal imports, ineffective implementation of minimum import duty, lack of institutional support etc are some of the problems faced by pepper growers. Production of pepper has become unremunerative due to depressed prices in the domestic and/or global markets coupled with increasing input costs. In this context, the study has focused on trends in area, production and yield of pepper, the seasonal effect of price and also the impact of the production of pepper on its prices to diagnose reasons for price fluctuations. The main objective of the study is to examine trends in area, production and productivity in major pepper-producing states and monthly price variations of pepper in India. The study relies on time series data. The data on area, production, productivity and monthly average price of pepper have been collected from the Spices Board of India. To study the behaviour of prices of pepper in different months, a seasonal effect has been constructed. To study the interrelationship between the price of pepper with its production and different months, a correlation technique has been employed. Regression analysis is being undertaken to know the present and future price trends of pepper. The time-series analysis of the monthly average prices of pepper is attempted from 2018-19 to 2022-23. The study revealed that there is a strong positive correlation between the prices of pepper and different months. The regression coefficient reveals that month significantly contributes to the growth and instability in pepper prices in the market. The study also suggests that, if the government could provide enough institutional support to stabilise the market prices, the pepper growers would certainly consider their crop as black gold.

REGIONAL ENERGY MANAGEMENT SYSTEMS BASED ON ESCOs: FEATURES OF CREATION AND APPLICATION

Excessive consumption and inefficient use of fuel and energy resources, unsatisfactorily high energy intensity of production of goods and services and their associated low competitiveness in domestic and foreign markets, in the context of a shortage of own energy resources and constant growth of prices for them, puts the problem of improving energy efficiency and energy saving of the regional and, as a result, national economy in the category of primary importance and relevance. The solution to this problem is achieved by creating a regional energy management system in regions (districts) on the platform of regional energy service companies, which allows, through a unified energy policy for the development and implementation of energy efficiency and energy saving projects in the region and the introduction of renewable energy sources, to more effectively address the problems of attracting foreign investment in the recovery and innovative development of the regional economy.

Crowding-in or Crowding-out: How Infrastructure Investment Affects Household Consumption

Abstract This paper constructs a dynamic model of household consumption decisions and accordingly designs empirical analyses to identify how infrastructure investment affects private consumption. The estimation results of provincial panel data reveal that infrastructure investment can not only indirectly promote household consumption through the income channel but also directly promote household consumption expansion through the expectations channel, with a significant “crowding-in” effect. However, the effect is significantly weaker in provinces with a lower housing supply elasticity, showing the “crowding-out” pattern through the wealth channel. Household survey data provide an explanation for individual heterogeneity. Infrastructure investment has a greater pushing effect on house prices in provinces with a lower housing supply elasticity. Rapid house price growth has significant “crowding-out” effects on the consumption of wealthy households. This not only significantly weakens the driving effect of infrastructure investment on aggregate household consumption, but also increases wealth inequality in the long term. This paper explains how infrastructure investment affects household consumption and the corresponding role played by house prices, providing empirical evidences for theoretical studies related to macro-regulatory policies in China.

From vacancy to verdancy? The impact of land options on the timing of vacant lot investment

AbstractLand vacancy is a persistent phenomenon leading to various socioeconomic issues in postindustrial cities. We examine the relationship between the timing of vacant lot investment and land options and compare two investment options using a quasi‐experimental design: housing investment that develops vacant lots into housing, and greening investment that converts vacant lots into gardens and reserves redevelopment options. By compiling a unique lot‐level time‐to‐event dataset based on a large‐scale vacant lot greening program in Philadelphia, we address the questions of whether and how introducing the option of greening investment to vacant lots can expedite initial investment and reduce vacancy. With the greening option, initial investment on vacant lots becomes less irreversible, thus reducing time in vacancy by 50% and elevating the hazard rate of investment by 80%. We test for the presence of the real options channel, identifying a decelerating (accelerating) effect of price uncertainty (price growth rate) on the timing of housing investment and insignificant effects of the factors on the timing of greening investment. Our findings suggest that vacant lot greening as an anti‐blight strategy is more effective to expedite investment in neighborhoods with elevated uncertainty, subdued growth rate, moderately lower income, and heightened vacancy.

Leasing in Russian agricultural economy: current stage of development

The purpose of the study is to analyze leasing sustainable development as the main investment mechanism of the Russian agrarian sector technical re-equipment. The author uses general scientific methods (analysis and synthesis, economic and statistical methods, graphical methods, etc.). The current state of the domestic agrarian sector technical equipment, the development of agricultural machinery leasing market development, issues and prospects for the use of agrarian leasing at the present stage have been considered. Considering the current situation in the domestic market, decreased prices for agricultural products and outstripping growth of prices for equipment, domestic agricultural producers need systemic state support to maintain the machinery and tractor fleet renewal pace. In conditions of agricultural machinery and investment resources for its reproduction deficit, leasing is one of the effective tools in solving the technical potential of the agricultural sector technical potential strengthening issues. The main promising areas of leasing mechanisms application, contributing to technical re-equipment acceleration in the agricultural sector have been proposed (sustainable development projects, national “green taxonomy”, “green” bonds; agricultural machinery market digitalization development; use of leasing mechanisms for the machinery and technology complexes development as a modern form of joint machinery use in agriculture). The proposed areas will ensure systemic development and acceleration of Russian agrarian sector technical re-equipment.

Assessing current state and prospects for housing construction development in the Far Eastern Federal District of the Russian Federation

Housing commissioning in the Far Eastern Federal District of the Russian Federation was low until 2019, which resulted in housing availability lower than the national average. The Government of the Russian Federation implemented the program on preferential mortgage lending “Far Eastern Mortgage” that led to an increase in the housing availability in apartment buildings in the macro-region, purchased on credit, while stimulating price growth at a rate outpacing the increase in household incomes. The purpose of the study is to develop proposals for improving measures to support the housing construction market of apartment buildings in the Far Eastern Federal District. The data on the volume and structure of multifamily housing under construction, the main factors of construction cost formation in the macro-region have been analyzed. The mechanism of mortgage influence with state support on the supply growth has been described. It is shown that the dynamics of supply is insufficient to achieve the average Russian values on housing provision of the population, in this connection stimulation measures require improvement. The high dependence of the housing market on mortgage lending programs with state support has been revealed. Based on the comparative analysis of the Far Eastern Mortgage program with other preferential mortgage programs, the article presents proposals for improving the measures of state support for multifamily housing construction in the Far East.

Extrapolative Uncertainty and Household Economic Behavior

We examine the uncertainty in households’ expectations regarding macroeconomic outcomes, namely inflation and the rate of nationwide home price growth. We document that people extrapolate from the instability of their personal and local environment when assessing the future volatility of these macroeconomic variables. Consequently, there are within-person spillovers in subjective uncertainty regarding different economic outcomes. This extrapolative behavior is more pronounced among lower-numeracy individuals, and it persists throughout the business cycle. People with more uncertain macroeconomic expectations are more likely to engage in precautionary behaviors regarding consumption and equity market exposure and are more concerned about credit market access. This paper was accepted by David Simchi-Levi, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.00820 .

Enhancing Analytical Precision in Company Earnings Reports through Neurofuzzy System Development: A Comprehensive Investigation

The object of research is the fundamental and technical indicators of companies after the release of the earnings report. This study attempts to address the issue of understanding the impact of fundamental and technical analysis indicator dynamics on profits and loss news releases. This research provides an in-depth analysis of stock price forecasting models, focusing on the influence of earning report seasons as catalysts for stock price growth. The study explores the relationship between key financial indicators, including earnings per share (EPS), revenue, and the maximum price observed in the 52-week period of the previous year (MaxW52). A trading algorithm is developed based on the adaptive neurofuzzy inference system (ANFIS). Through a comprehensive analysis of the neural network’s training sample, it is concluded that abnormally large negative indicators have a profound impact on traders’ emotional reactions. This results leads to a hypothesis for further research, suggesting that report indicators may be processed by computational algorithms, potentially including artificial intelligence (AI). Consequently, the emergence of emotional trading robots managed by investment funds becomes a crucial area for investigation. Understanding the behavior of these algorithms enables proactive decision-making, allowing traders to leverage their knowledge and sell-purchased securities to these algorithms before their transactions occur. The implications of this research shed light on the evolving landscape of trading strategies and the role of emotionality in financial markets.

Consumer Price Index in the Czech Republic – New Sources and Data Processing

Consumer price index has been in the centre of interest for many years, since being published in 1990s in the Czech Republic but recent price growth raised more questions on methodology and data sources used in price statistics. Users are interested not only in the figures itself but also in statistical issue influencing interpretation and the quality of consumer price index that is often used as an approximation of inflation rate. The paper introduces price statistics compiled by the Czech Statistical Office and it specifically focuses on data sources and in particular scanner data. The paper explains how advanced statistical methods such as machine learning are implemented in official statistical production. We think that the official statistics is being on the historical junction where modern methods are going to be implemented. Our paper shows the usage of machine learning procedures applied on scanner data within consumer price index. Used method is based on logistic regression and powerful Python solution and that provides fast and high quality results.

Macroeconomic drivers effect on housing sale prices in China

Purpose The purpose of this study is to conduct a thorough empirical investigation of the intricate relationship between urban housing sales prices and land supply prices in China, with the aim of elucidating the underlying economic principles governing this dynamic interplay. Design/methodology/approach Using monthly data of China, the authors use the asymmetry nonlinear autoregressive distributed lag (NARDL) model to test for nonlinearity in the relationship between land supply price and urban housing prices. Findings The empirical results confirm the existence of an asymmetric relationship between land supply price and urban housing prices. The authors find that land supply price has a positive and statistically significant impact on urban housing prices when land supply is increasing. Policymakers should strive to strike a balance between safeguarding residents’ housing rights and maintaining market stability. Research limitations/implications Although the asymmetric effect of land supply price has been identified as a significant contributor in this study, it is important to note that the research primarily relies on time series data and focuses on analysis at the national level. Although time series data offer a macroscopic perspective of overall trends within a country, they fail to adequately showcase the structural variations among different cities. Practical implications To ensure a stable housing market and meet residents’ housing needs, policymakers must reexamine current land policies. Solely relying on restricting land supply to control housing prices may yield counterproductive results. Instead, increasing land supply could be a more viable option. By rationally adjusting land supply prices, the government can not only mitigate excessive growth in housing prices but also foster the healthy development of the housing market. Originality/value First, the authors have comprehensively evaluated the impact of land supply prices in China on urban housing sales prices, examining whether they play a facilitating or mitigating role in the fluctuation of these prices. Second, departing from traditional linear analytical frameworks, the authors have explored the possibility of a nonlinear relationship existing between land supply prices and urban housing sales prices in China. Finally, using an advanced NARDL model, the authors have delved deeper into the asymmetric effects of land supply prices on urban housing sales prices in China.

Cross-country comparisons in health price growth over time.

To examine how the United States compares in terms of health price growth relative to four other countries - Australia, Canada, France, and the Netherlands. Secondary data on health expenditure were extracted from international and national agencies spanning the years 2000-2020. International price indices specific to health were constructed using available international expenditure data and compared to existing health-specific national and general international price indices. Health expenditure data were extracted from the Organization for Economic Cooperation and Development (OECD) database. We obtained a time series of health price indices from the national agencies in each of the study countries. We find meaningful variation across countries in the rate at which health prices grow relative to general prices. The United States had the highest cumulative health price growth compared to general price growth over the years 2000-2020 at 14%, followed by Canada and the Netherlands. Unlike the other study countries, health prices in France grew consistently in line with general prices. Price growth for health care paid for by public funds and households grew at different rates across countries, where price growth was higher for public payers. US households faced the greatest mean annual price growth. The choice of price index has major implications for comparative analysis. Despite their widespread use internationally, general price indices likely underestimate the contribution of price growth to overall health expenditure growth. We find that in addition to its reputation for having high health price levels compared to other high-income countries, the United States also faces health price growth for goods and services paid for by government and households in excess of general price growth. Furthermore, US households are exposed to greater health price growth than households in comparator countries.

Temporal Analysis of Bael guda (Aegle marmelos) Prices in Central India

This research presents a comprehensive analysis of Bael guda prices in Central India over a 12-year period, spanning 2010-2014 and 2015-2021. The study delves into the intricate details of pricing dynamics, regional variations, and temporal fluctuations within the Bael guda market. Quarterly price data was collected from various markets, including regional and district levels, to analyze long-term market patterns and price trends. The research highlights the regional disparities in Bael guda pricing, with Shivpuri consistently emerging as the district with the highest average prices, underscoring the influence of factors such as local availability, demand dynamics, and transportation costs on pricing variations. Additionally, the study offers insights into the stability and variability of prices, with the 2015-2021 period exhibiting greater fluctuations, as indicated by higher standard deviations and coefficient of variation values. Furthermore, the research evolves price growth patterns, with remarkable growth rates in certain markets, such as Mandla, Dhamtari and Sheopur (M.P.). This comprehensive analysis not only enhances our understanding of the economic aspects of the Bael guda trade but also provides valuable insights for market stakeholders and policymakers, facilitating informed decision-making in this niche market.

Decomposition of medical imaging spending growth between 2010 and 2021 in the US employer-insured population.

Medical imaging, identified as a potential driver of unsustainable US health care spending growth, was subject to policies to reduce prices and use in low-value settings. Meanwhile, the Affordable Care Act increased access to preventive services-many involving imaging-for employer-sponsored insurance (ESI) beneficiaries. We used a large insurance claims database to examine imaging spending trends in the ESI population between 2010 and 2021-a period of considerable policy and benefits changes. Nominal spending on imaging increased 35.9% between 2010 and 2021, but as a share of total health care spending fell from 10.5% to 8.9%. The 22.5% growth of nominal imaging prices was below inflation, 24.3%, as measured by the Consumer Price Index. Other key contributors to imaging spending growth were increased use (7.4 percentage points [pp]), shifts toward advanced modalities (4.0 pp), and demographic changes (3.5 pp). Shifts in care settings and provider network participation resulted in 2.5-pp and 0.3-pp imaging spending decreases, respectively. In sum, imaging spending decreased as a share of all health care spending and relative to inflation, as intended by concurrent cost-containment policies.

Detection of financial bubbles using a log‐periodic power law singularity (LPPLS) model

AbstractThis article provides a systematic review of the theoretical and empirical academic literature on the development and extension of the log‐periodic power law singularity (LPPLS) model, which is also known as the Johansen–Ledoit–Sornette (JLS) model or log‐periodic power law (LPPL) model. Developed at the interface of financial economics, behavioral finance and statistical physics, the LPPLS model provides a flexible and quantitative framework for detecting financial bubbles and crashes by capturing two salient empirical characteristics of price trajectories in speculative bubble regimes: the faster‐than‐exponential growth of price leading to unsustainable growth ending with a finite crash‐time and the accelerating log‐periodic oscillations. We also demonstrate the LPPLS model by detecting the recent bubble status of the S&P 500 index between April 2020 and December 2022, during which the S&P 500 index reaches its all‐time peak at the end of 2021. We find that the strong corrections of the S&P 500 index starting from January 2022 stem from the increasingly systemic instability of the stock market itself, while the well‐known external shocks, such as the decades‐high inflation, aggressive monetary policy tightening by the Federal Reserve, and the impact of the Russia/Ukraine war, may serve as sparks.This article is categorized under: Applications of Computational Statistics > Computational Finance Algorithms and Computational Methods > Computational Complexity Statistical Models > Nonlinear Models